{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data manipulation with numpy: tips and tricks, part 1\n",
    "\n",
    "Some inobvious examples of what you can do with numpy are collected here.\n",
    "\n",
    "Examples are mostly coming from area of machine learning, but will be useful if you're doing number crunching in python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function # for python 2 & python 3 compatibility\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. numpy.argsort"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorting values of one array according to the other\n",
    "\n",
    "Say, we want to order the people according to their age and their heights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[49 45 44 52 44 57 46 49 31 50]\n",
      "[209 183 202 188 205 179 209 187 156 209]\n"
     ]
    }
   ],
   "source": [
    "ages = np.random.randint(low=30, high=60, size=10)\n",
    "heights = np.random.randint(low=150, high=210, size=10)\n",
    "\n",
    "print(ages)\n",
    "print(heights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[31 44 44 45 46 49 49 50 52 57]\n",
      "[156 202 205 183 209 209 187 209 188 179]\n"
     ]
    }
   ],
   "source": [
    "sorter = np.argsort(ages)\n",
    "print(ages[sorter])\n",
    "print(heights[sorter])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "once you computed permutation, you can apply it many times - this is fast. \n",
    "\n",
    "Frequently to solve this problem people use sorted(zip(ages, heights)), which is much slower (10-20 times slower on large arrays)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing inverse of permutation\n",
    "\n",
    "permutations in numpy are simply arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 7 4 9 2 8 0 5 6 3]\n",
      "['a' 'b' 'c' 'd' 'e' 'f' 'g' 'h' 'i' 'j']\n",
      "['b' 'h' 'e' 'j' 'c' 'i' 'a' 'f' 'g' 'd']\n"
     ]
    }
   ],
   "source": [
    "permutation = np.random.permutation(10)\n",
    "original = np.array(list('abcdefghij'))\n",
    "\n",
    "print(permutation)\n",
    "print(original)\n",
    "print(original[permutation])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Inverse permutation__ is computed using numpy.argsort (again!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a' 'b' 'c' 'd' 'e' 'f' 'g' 'h' 'i' 'j']\n"
     ]
    }
   ],
   "source": [
    "inverse_permutation = np.argsort(permutation)\n",
    "\n",
    "print(original[permutation][inverse_permutation])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__This is true because of two facts__:\n",
    "\n",
    "1. indexing operation is associative (dramatically simple and interesting fact):\n",
    "   ```\n",
    "   a[b][c] = a[b[c]]\n",
    "   ```\n",
    "   provided that `a`, `b`, `c` are 1-dimensional arrays\n",
    "   \n",
    "2. `permutation[inverse_permutation] is identical permutation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "permutation[inverse_permutation]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Even faster inverse permutation\n",
    "\n",
    "As was said, numpy.argsort returns inverse permutation, but it takes $O(n \\log(n))$ time, while computing inverse permutation should take $O(n)$.\n",
    "\n",
    "This optimal way can be written in numpy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6 0 4 9 2 7 8 1 5 3]\n",
      "[6 0 4 9 2 7 8 1 5 3]\n"
     ]
    }
   ],
   "source": [
    "print(np.argsort(permutation))\n",
    "\n",
    "inverse_permutation = np.empty(len(permutation), dtype=np.int)\n",
    "inverse_permutation[permutation] = np.arange(len(permutation))\n",
    "print(inverse_permutation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing order of elements in array\n",
    "\n",
    "frequently it is important to compute order of each value in array. \n",
    "\n",
    "In other words, for each element in array we want to find the number of elements smaller than given."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.69378073  0.47532658  0.11610735  0.89700047  0.04700985  0.24701576\n",
      "  0.29017543  0.27857828  0.62900998  0.08187479]\n",
      "[8 6 2 9 0 3 5 4 7 1]\n"
     ]
    }
   ],
   "source": [
    "data = np.random.random(10)\n",
    "\n",
    "print(data)\n",
    "print(np.argsort(np.argsort(data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__NB:__ there is scipy function which does the same, but it's more general and faster, so prefer using it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.,  6.,  2.,  9.,  0.,  3.,  5.,  4.,  7.,  1.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import rankdata\n",
    "rankdata(data) - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IronTransform (flattener of distribution)\n",
    "\n",
    "Sometimes you need to write monotonic tranformation, which turns one distribution into uniform.\n",
    "\n",
    "This method is useful to compare distributions or to work with distributions with heavy tails or strange shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class IronTransform:\n",
    "    def fit(self, data, weights):\n",
    "        weights = weights / weights.sum()\n",
    "        sorter = np.argsort(data)\n",
    "        self.x = data[sorter]\n",
    "        self.y = np.cumsum(weights[sorter])\n",
    "        return self\n",
    "        \n",
    "    def transform(self, data):\n",
    "        return np.interp(data, self.x, self.y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's apply __Iron__ transformation to data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sig_pred = np.random.normal(size=10000) + 1\n",
    "bck_pred = np.random.normal(size=10000) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4nJlXAN+r1omIrcCtwFZgG3B3RPhbhST1SU+Bm5nHM/NotfwG8CPgMuAmYH+12X7g5mp5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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108a65590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure()\n",
    "plt.hist(sig_pred, bins=30, alpha=0.5)\n",
    "plt.hist(bck_pred, bins=30, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see that __IronTransform__ actually was able to turn signal distribution to uniform:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x108bedbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iron = IronTransform().fit(sig_pred, weights=np.ones(len(sig_pred)))\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(iron.transform(sig_pred), bins=30, alpha=0.5)\n",
    "plt.hist(iron.transform(bck_pred), bins=30, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How IronTransform works\n",
    "\n",
    "To turn any distribution into uniform, you should use mapping:\n",
    "$x \\to F(x)$, here $F(x)$ is cumulative distribution function. \n",
    "\n",
    "In other words, for each point $x$ we should compute the part of distribution to the left. \n",
    "This was done by summing all weights to this point using `numpy.cumsum` (we also had to normalize weights).\n",
    "\n",
    "To use the learned mapping, linear interpolation (`numpy.interp`) was applied.\n",
    "We can visualize this mapping:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x108df0750>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x104a07090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(iron.x, iron.y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IronTransform and histogram equalization\n",
    "\n",
    "The same method is applied in computer vision to 'improve' low-contrast images and called [histogram equalization](https://en.wikipedia.org/wiki/Histogram_equalization)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "image = np.array(Image.open('AquaTermi_lowcontrast.jpg').convert('L'))\n",
    "flat_image = image.flatten()\n",
    "result = IronTransform().fit(flat_image, weights=np.ones(len(flat_image))).transform(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gn1GtVoWQVatVdDodTE1NoVgsihaLLmokMRxTfr8f9Xpd3PdMgM96UsEwOqbY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Vk+TqcTHo9HnFrSmXnRxgDlAQo/Q3mmHab90HUAPIuU/zejq1Q6QdS9msqrbRvvywGUpEjv\n379fZsqwHrO+vh5erxdtbW04f/48nE6nnB3qLz4X7QV1PkEsu92OD3zgA3A4HJienhZqcFdXF7q6\nugRRd7lc0khAA2MsiOeQWIPBIIGx1gm0QblcTihbBOOYVdBrUCgUEAgEMD8/L51O19bWUF9fL/N0\nmE3gntC/KRaLOHHihGQSSfXlqIrZ2VmkUilceeWVMJvN6O7uRiwWkwCnUCjgy1/+sshvZ2cnDAYD\namtrsbi4iEKhNFdnx44dyOfziMVi4kN4vV40NDRgYWEBU1NT2LZtG2ZnZ/HKK6/g/vvvFzmiDvV6\nvWX7z8YDHIhrs9mwubkJr9crM/DoB4TDYTQ0NGBmZkaG6rLJUWNjI9bW1jA2Noauri5MTU2hrq5O\nWqzX19djeXlZumCeOnUK6+vrGBoakqwndXk8HofH48GvfvUrtLa24ty5c/jVr36FZDIptoZ+KCmC\npIb19/ejs7MTra2tuPXWW6XFts72ajob/RKeGdb3BINBNDQ0YGNjA1dffTUeeOABGAwG0RsABGxm\nALK2tiaADc8FZcXn82F+fl6al/j9fgFr2O6a85oGBwcxMjJS5q/qs63PuD7rZCa81fW2C260ItRT\nSUnZIGqsU9hE/RnF8z488Bpl1l0qaPDpqBGVp8KlE0LH3u12l/Fb+T1UmESJNJrEw8yIH9jq9qTp\na8BWb3oA6OnpQTQaFaXD9Cr/1AJFp6sSOafTRhSc66HpaFxfIlOJREL2gAV+r7zyCs6ePYsvfvGL\nIuAUVK4HGx3ceOON+MEPfoB77rkHa2trmJqakiFT+XweR44cwenTp4WL+a53vUuohSy403QkPiMd\nSY0CstEA3437CJQchnA4jNXVVaFTnD59Gvv374fFYgEANDY2lgV45MePjo5iaWkJQ0ND4pj39/dL\n0Kkzh+xoxLogygT3gUaBa8/90qgmHXXdYUw76RqB0S0lGfwxYDWZTOjr68NPf/pTHDx4EC+//DJS\nqRQOHTok633zzTfjH/7hH+D1ehGJRHD+/HmcOnUKDz30EC5cuIDh4WF0dHTgJz/5icwj0gpHZ2qA\nLTCCwTcdbk4f1kiLprTpFDrPDw277iaoM0B6/3XwR8oEjbheQwYqBoNBGiqwdoqBtKa28jl1sKr1\nBN+TMkPHlDqKCCcbYDgcDiSTScnuUB68Xq8EW0SAq6urZbo568J0wKWDO9bVEVUlmkcqKgCZkM09\n0ZQhDSJxzf5YROyda8vQG41GfOADH5Ap7YcPH5a6CSL+VqtVOjiRAjsxMQEA4sCura1J97V0Oo2x\nsTFpycxagvb2dqTTaQwNDclgR3ZYamtrw/DwME6ePInbb78do6OjEtwCkGwnawk9Hg9isZjURdjt\ndqyurqKnp0eocZubm9izZw/m5uZgt9tRX1+PXC4n0+uBknPEGWrMxOqAhGeHa6XPNbDlrGimhQaB\n+HnqN9pSXXNHmlwoFEI4HMbw8HCZTdWBEZ/hu9/9Lj71qU9heHgY/f39AkKsrq5iY2MD//RP/4T9\n+/ejtrYWHo8HAwMDZYES36ESJNBABH+2srIiNU6BQAD19fUS6OZyuTJnN58vNQlpbW2VM+l0OiVr\nRODHZrNJG92GhgbRR6Ryc6/5HNQJ2ndhRru6ulp0Y21tbVkb8dnZWaytrQkw2NXVhebmZtx4440w\nGAwS1KbTadF9PT09GBkZwalTp3DjjTdiYmJCagbX1tbgcrnQ1taGs2fPoru7W9qR9/f3iy7t6ekR\nejvBqcOHD+Pzn/88kskkvF4vDhw4gJGREdx///0YHBwUmhcDLQZpBkOpkQ99H4/HI00ORkZGsHv3\nbhQKWwXvrN1klzxSA3O5nFD1urq6cOzYMQEPWC8zNzcnNVQbGxtwu90Cps3NzaGjowMPP/xwmd/H\nEoGmpibYbDb09fXhfe97HyYmJlAsFtHd3S1rt7KyUjYzhjaHGSqTySTgJ31e1s0SpGdGMJvNor6+\nHtFoVPwBt9st/goAfPKTn0QqlUI4HBb2DG2by+XC3NycDJOn/Sb13+12w+fzCftjc3MTi4uL6Ojo\nkCGxADA/Pw+XyyWNCSqTEm8FtmlW1Ztdb7vgpra2Vjp+0AFk1EouKrAVCNBYUyjpaGrUE9iK8jQH\nUQcTdMjo8FBRsxUmgwfNg9cpd96DjpGmxAFbio5ZITrsmh+rFeNf/MVf4JFHHpEDyg47zPTwXTSX\nuVIIstks3G63HCQiOcw+EYXme9BZ4oAkZqpuuukmvOc970E8Hsf58+dx4MABcTb5HAwOM5kMvvSl\nL+FjH/sY3v/+90v7xc3NTfziF7/A6uoqfvOb32Bubg7nz5+X9qXcP3beoSNJo+fxeMQQuN1uOBwO\nfPOb30R1dTXi8Th+/vOfY2JiAsPDw2WFsAx6yAXlWhCVIVpOLuza2hqWlpZQX18viCaHghLxZEtX\n/r6WBVL2tBEhagtA9osXETiiFqRL6WBYZ2ZoqHQWTiuym2++GfX19fKcbW1teP7553HNNdcgmUzi\ntddeQ09PD5aXl+FyuRAOh/Fnf/Zn4kw/99xzOHbsGMbHx6WpwH333SfPzqJUyjSfmY4H6RJ02Glw\nKCdAebcXYCvoqXTkmdWgQ8PfrXQwbDYbMpkMamtrZb8LhYJkOVlLs7GxIRkRPgv5xjrjxD3S2SO9\nv6R7UhdwwjcVPgAsLCygrq5OaCcApD1vPB6XjkI0bLFYrCzzpemnpMjpeiAG9hroYTa1UChIpiyd\nTouDzTOms7JE1ymHPMvvXL//uv322/Hiiy+iWCxiaWkJ6+vr0v3syJEjkklnPQv1aaFQkC55HKTI\ns2cwGDA2NibIczAYFFoZz5Hb7caJEyewe/duWK1WXLx4Ec3Nzdi7dy+y2SwaGhqQTqcxPT2N6upq\nCTBYG8jMUSgUQjqdRnNzM2KxmBR1M7Pq9XrR19eH+fl5tLS0wO12Sza9WCyKQ7Nv377fqXeopHBq\nloEOCjTooZ1vDWZpUIOXBjd4Jg0GA5qbm9HU1CRnkU5/ZXaH7I7//u//xsGDB9HR0SFAVaFQwKc/\n/Wl8/OMfx9LSEuLxOMLhcBmoykBLAwM8PwRISdtZX1/HRz/6UVm7CxcuIBgMor29vQygot6pBMHS\n6bQ45nxvgmNst8vgiGecndjoZxAIqdS5wWBQ6l+LxdL4gFOnTsFoNMLtdks7bbvdjqWlJTgcDly8\neBF79uxBPp/H4cOHJQDPZrNSk8Euat3d3XC5XPD7/YjFYhLcra+vo6urS7r7kZI1MTGBjo6OsqJ7\nthleW1vDbbfdJjZxYWEBc3NzeO655/Dggw/C6XQiEonA4XBIVoA+GAEq0sA2NjbQ3d2NTCaDAwcO\noKqqCqOjo2JXaR9YT0X5IpXR6XTKvpCuzuGkO3bsEHscDofFpygWi7jiiiswPj6OT3ziEzJAN5fL\nYXx8HB0dHZL1WVtbQ01NjXQmBICxsTF5L/oHzJqcOXMG/f39MvwVgPhwbLlNYNZoNOLixYvo6+uD\n0WjEY489hptvvlk67AIlv/HAgQN4/vnn0dPTA7fbLd2Hp6enpTFJLpdDS0sLwuGwyNnU1BSam5sl\nS7e+vo7Ozk4pq6DvzeYnU1NT2L59O1555RXJoGUyGWmpr0Fi6g9ef8hOve16fnJT6OAAW+gwHXMq\nFx3gaGRf1ykQsdEBCCP4ypSXHiLEBWVHEDpxdI6p5PhvlalzXVcBbNF2eFComPlOfAamHYeGhuBw\nONDa2iqKshJ51UEN78HPaOPAOQm8PzMFVIRUpOyjXywWyzipTIGaTCbs2bNHAiPSBIGS47a6uooH\nHngA+/btw49//GOcOXNGIvnFxUWEw2H4fD588pOfxL333ouf/vSnyGQyori5Z0SjuWZWq1UCXion\ng8GAb33rW7jnnnvw13/915ifn8dHP/pR3HDDDfI+rMsoFAoYGBjAFVdcIXNAAoGA3LNY3GqhqedB\nJBIJGI1GxGIxZDIZHDlyRHjSVGCVmUOdIeOecP9ofPTP2OKb9VKV8k7nU+8/95Frp43sI488gr/8\ny7/EgQMHkEgkhDqXSqWwfft2RCIRTE1NSYcWADhy5Ah+/etfI5vN4qabbsJ9992H//qv/5IWlh/4\nwAck26czlHxWyjwDMl3IznOs09b8PFth6nvzLJFawu/SzjvfX2cySMuiQadcer1eGV5YX18Pt9st\n1B+n0ympcYvFInORNKWO8lFZT8C5BVTCNA4ccMcgit3qWLdAxL+hoUHqJXR9lS7+t1hKk515b8oV\nMzx6pgfPKZ1OIpFE6+h46cCYe6nXWAM+71xvfYVCIXi9XnEC6RCmUikMDg7CZrMhHo/D7/ejvr5e\nEG7KnslkQiQSgdPplJa8+Xwe6+vrwmOPx+Nob28XoI089gMHDkiWmjNTFhYWcOLECQSDQdl7tsZl\nNoJNMxwOhwQ6dHAp82yNy4AlFouhqqoKfX19Mldqc3NrforP50NVVZVkFvQ5ZlCj7VtlZlpTd7Xd\n1llbYKtZCm2arm+lTWRgZTKZhI5Enayp6Jubm/jc5z6H/v5+/PM//7O0gCbTgbVU27Ztw759+/De\n9763jBLHi7qH70IAkeeIeunpp5/GQw89hK997WtYWlrCnj170N7eLutBPVcsluisbW1tYt8ZmOgM\nFwDRDQyEAciog9nZWdEjXDsdNPE7WcjOgCwWi6Gvrw89PT3I5/PSlIfBQHV1NXbv3i2yR/Cmv79f\n/JSuri4Eg8Gy9bl48aLoK8rm6Ogonn76aQQCAWxsbCAWiwEoOdYMamKxGDY3N+F2u5HP5zE/P4/5\n+XkUi0U0NzfjwIED+MY3viFrfuedd8qsO54l+nnsHAtAslMcnhmPx+FwOIRemslk4PV6pY6Nc2Ba\nW1vh9XplsK3T6cTc3JxQ1T0eD0wmE1ZWViSQSKfTcLvdaGhokACRgUwqlZL36enpQU1NDSwWCxoa\nGmC326V5iMvlwsDAgDSh6ejoALCVPe7p6RHAi/5RoVCir4dCIZEJg8GASCSCjo4O+Hw+rKysYN++\nfdJ0iANgs9kshoaGAEBKBtjNkTN6GOyT8VNXV4doNCp1V9lsFk1NTVhYWMDq6iqSySQaGxulI2NL\nSwusVqsMDW5ra5NmCTt27Pidhgk6S8pzQ5l5q+ttl7kBtlLomoZCh0c7VkA5nUJnJHiw6TRT6VKR\nahSZh56/q9PiOpCgU60Vmk5LM8NEx1d3HCM6T+eOf+oMjlbUZnNpkm1vb6+092TwkslkkMlk5NBr\n+oHmGTPtbDKZJN3IwkMi2ewKQgcdKG/qQAebRX5UJHwHXUyay+Vw+eWXY3h4GE8//TSA0jwBm82G\nI0eO4KWXXkJPTw96e3sxODiIjo6OskyXrqfgd+ggl/tHmp3FYhEFWywW8cQTT2D//v2CftBBzufz\nMtskHo+LMvN4PILcs1tRc3Mzdu3aVSZzp0+fxujoqGRwuO8ejwdNTU24ePGiBCMMFrlXWka1/PLf\nNGJOZ51GQHcFYiCj0U4dOPD+DocDn/jEJ+QcUR68Xi/C4TBcLhcOHTqEpaUlbGxsYHR0FLfccos4\nAwyuv/e978lgOJ0l0TQRXVCrnRNtrJl50b9Ho0xjzPOhjTEDcCLF+pzpTCoDI72eXK+rrrpKjBwd\nep19YqaJVB2eAz4f34nGUgdhPEsMXpjhZRctynI4HJazphFtDvVjFoj1OZR7ImM6U8x3Y4bVYrEg\nmUwK0kf9lM/nxZAwwOe5Arba4GvKkHZG37n+8MVgZmRkBB6PR+ZeWa1WzMzMIB6PlwFHWk6ZqePv\n0FGw2WxCm2Q9IYt62fBENwngLBxmKmpra2G1WnHs2DFUVZXa7K+urspZoQ5ZWFhAU1MTrFYrent7\nMTExIcM+nU4nJiYm0Nvbi0gkgp6eHqlFaG5uRm1trdRdEoiko1dZc8Oggs4z5Q7YOuNcS00DB7bA\nSJ4ZAp6VgKIGNyn7lQN/NRhJOW9oaIDP55Mgg+fz9OnTOHLkCLZv346GhgbU1dUJCKODGE0914Ga\nDiCo+ziMs6OjA8ViEaOjo2hvbxemQyUNVvsOBOPIRCEzwOVySZE71zsYDGJ5eVmcXr6/xWIRehT1\nKr8jm81KZysWiDOr53a7BSChXuMQaup0NkYg02JiYkJobDpAcjgckk1ZWlpCVVUVrr76agBbJQSk\nM5F1wXbcm5ubWFhYwMDAQFnHV7PZLLVgHo+njDlDmeNsJ6PRKC2jScWjE14oFGSmIDP12WwWvb29\nyOfzmJqawvz8PCwWizT/MBpLc//S6TTa29tx7tw50bXU1alUSga4trS0oLa2VgBRAhWjo6O45557\nxP/U9oXOPQABuo8ePQqHw4HFxUXs3LlTal9mZ2exb98+ZDIZOYvc+1wuh9bWVkSjUWk5PTIygkAg\nINn9iYkJ+P1+RKNRWCwW6T4bj8cFvGEn2kgkIraZM+QIymi2Bhs0VVdXy6D0gYGBMp/rve99rzwX\nZ02xZp4g3psBC5XZ2DfV0b/3p/8PLjqkVBxUHgBEidEJocLRKW7+qTnq/Lmm+fDSwREdfH6fVjpU\nCjyIPIxcYP6bvg/rKPjvDBRMJpMg+Sz+4vOyHuipp55CV1eXdBJhQHPvvffi4YcfBgBB4vgOzALR\niDKSZzcOdt7h91BR0Fni2uhgQzuaAMRI65k5fL/5+Xl8+9vfRjKZxEc+8hGk02nZp/b2dng8HoyO\njuLxxx/Hj370I3z1q1+V7yAlQlOTtIFgsTQLJTc3N3HddddJ8R85xqQ3kPJjtVqFpsa9mp+fx+jo\nKE6ePIlUKiUoWG9vrziy2WxW7lsoFHDgwIGyrBzTzhyyqBVlZQCuDat2xHWgxM/oIJeGnHtEmolG\nPzU1w2Qy4V3vehduv/12JBKJsowSn6+vrw+PP/64zFa56qqrRPlRKX7nO9/Bhz/8YVx//fU4fPiw\nyLTO1vAZAEg2QGfeaJT12aTcA1s0UV3wqDM0/JOIoy6g1aioDn5Yr8K14PpRVmm0KAt0LIvFogxX\nLRQKQsfkWaCjxlxbJIMAACAASURBVDNImef/sy6LIAKdFv6+zWYTaoku+KTzR5mhc6qpeAya+d6a\nosfAh/xu6jgaT+4ZAxsdHOlGFJXB9h8q1HzngiC2fr9fglnSz2w2G1paWuDxeDA3N4dUKiXdhLQN\nIaJstVqF08+MD3/G/bFYLPJ3dk3isNdYLCZBbjabRVtbm1BY2NKd9F6fz4fGxkaYTCbMzMzAYCh1\nCBscHJQs5o4dO9DQ0IDGxkYMDAxg27ZtCAQCaG1tledlE4HZ2VlBmHkWNjc3cezYMVy6dKnMceVV\nyczQGRnaUdoUnfWtDJD4c62LeE/ev5L2DZRq0Y4fP45MJoPu7u4yPdva2oqqqiq8/PLL+Jd/+Re8\n9NJLePLJJ8vOiQY2eW74jLSvPGOFQgHbtm2T9snpdFrqZQEIyEn9qetkVldXsby8jFAoJEwKOtg6\nYKMeoZ3VLBCz2Sx2uBJ8opPPoIC1G5RHgrSkuHJv2R6ZwTwL5zc2NjA5OYmDBw9Ki3t+DwMozkTq\n6OgooyvpdWT9yrlz57C8vAyDwYDe3l4Bo3iGXn/9dezcuROtra0IhUIAgPHx8TLHXvt/fCY667W1\ntXJG+AwAJBADSmMR8vlS97JAIACr1QqPxwO73Y5oNAq73Y7l5WXpSMiW6blcTkYp2Gw2jI2NSRAw\nMTEhjTk4GJ26gftGnw+ABLrFYhEDAwNCZT1x4gRMJpPUJa2srMhcHjYjMJlMWF1dFQYP65e6u7ul\nHpS1sg6HQ+YGDgwMCBWN9oMyVl9fD7vdLjaN+8yOfqRsk6LNOlC32y1sGNY1hUIhyRzF43Fks1nJ\nmKVSqTK2ivbVC4UCFhcXf6+OfttlbvTL6L8TgdepbC44BVg72pqrpzMNWskVi8WyBgN0shgU6ICI\nG0QFQaoAFZqmofH7AMhnWASoqXT8OVDe6c1kMmH37t2YmJgQhcf7feMb34DVapV5JQzguFbAVhBI\nR29hYUG6pfHA8H3I+adDw8CHtAW9HyyepjNFRcS1C4VCeOihh3Dy5ElcffXVaGlpESfqM5/5DIaG\nhrC0tISRkRG8/PLLOHPmjDibVC46s8Q9BcoDRtIsHnroIdhsNmzbtg1XXHEFhoeHcebMGaFrcC9p\njNkhjAaT6MT6+jpWV1fh8/kAlILQ06dPo7GxEbW1tbjsssuQSqXKnFROQeaUaTqaTqdTHAnKj6Ye\nMlDRNEmtgIvFYtnEXp0R1Agh5ZD7bTKV2m4ODQ3hxRdfRF9fX9l9mcJ98cUXYbfb0dbWJijf008/\njSuvvFLS6l/+8pfxi1/8Ap/4xCdgs9kwOzuLPXv24OTJk/LMDNrpbBsMBkFZtPPPTF/leWJRJA2v\n7kCoUV2eU/69sqU214znj2vb2dkpMz/4zNpgVAIezAKyrstgMJQ1R2BAXygUpPaPdMlisVjWWY+G\nlWtD9Jb8ccozg0TqHT30ly1VeR51QMk1JkJNp4cNLRisJZPJMv2iZ+FoJFrLngaT3rne+lpZWREd\nBZScILafjcVignSyFT+RcAbCPDM08rQj0WgUra2t4mQCEFpJY2Mjkskk/H4/ampqMD4+Lhz/UCiE\nYrEo2fFwOIxAIIDJyUnU1dUhk8mgvb1dahxIV2QTDiLXc3NzGB4eRlNTkxQhm0wmtLS0SHDe1NSE\n6elpqRtgp0EdJFxxxRViE0i91bJHWdO0NNoXncmpRLN5XqnzaLe0vtDZYK0jaIvS6TR+/OMf433v\nex86Ozvh8XjkLOzduxc33XQT0uk0VlZWMD8/j8OHD6O3t1eAEF7MsGr9RECGtqdQKODv//7v4ff7\n0dPTg0OHDqGhoaEsi8LnJB0xn89LYx/W3fC9dF2w0WhEMBiEy+WCzWZDIBAQu0DZJKKu29wTgDp3\n7hyam5sly8dBkblcqdXx3NwcEomEBF76/sViETfccAOOHj2KQCCAbDaLpaUlmc/EAdKhUAgNDQ2i\nx2w2G9ra2qQzGscMEBAkdWxychJVVVVC9TSbzRgdHcXBgwdlzXbv3o2ZmRls375dnPg77rgDr732\nmnwfi/s1w2V9fV0yTg6HA0tLSzh37hycTid6e3sle2Q0GtHW1oa2tjZMTk5iaWlJGgaw+RAdfxb8\np1IpeL1ejI+Po62tDVVVVYhEItIZV88LTCQSuOOOO5BIJMpsH8+CBjppt9xuN9773vdifX1dsq1j\nY2Ooq6vD8ePH4ff7xY4xC+NyuXDu3DlkMhns2bNHsna0W6yxy+fzcDgcSKfTqK+vl/NFe8vAmGeS\nTaR4bunfms1muN1uqSP2er3CWnI4HNJoJZfLoampCXNzc9KOmjP5dAygWRv63ykrb3W97TI3lYEN\no1gd0FCgtCOkDXLlPYAtxEyjGpWIOf9d07f4DEA5957OBg8MMyVUwhqF5kHjPXSwpv9NozaV9Dki\nFnTsh4aGfqdwXQdzLF41m80yt4T3Jy1HZ2DoIBUKBemWoilTmg9NRIrFhlTA+XweX/rSl5BIJPDg\ngw+iWCxKx6jnnntOZjqwY9fPfvYzmctBlIh7xMJn7u3GxoakLIPBoHSgqa+vRyKRwC9/+Ut885vf\nFOVNg8tArFgsSrFuKpVCQ0MDBgcHYTQaEYlE4Pf7cf78eVy8eBEGgwH79u1DbW2tOJ1smclgmP30\nSc3Q6BOzUTqDw0CaMqmR88q/UyYpe5QNHdxoSgf3/vLLL8djjz0mDhXlWTvHDQ0NksHo7+9HU1MT\nvv3tbws312g0Sop/ZGQEmUxGBpJ1dnYK+sTaI2bUvF4venp60N3dLVkgFlzSgLG4H4DUa2njoNFW\nfR514KKLRPXZoaywcQRnvehMksVigcfjKXsm0hN5PolwVjoulAM6cHw2fp4ABgOiZDJZtvak71gs\nFkHF+M78Dq4N34m/Q3SMxoT/T1nkjBIGU+TJ19XVybtTZ2l9p9eNulTL1jvXW18MUk0mE5aXlxGL\nxQSJZVON5eVlcaRcLpdQJIGtjAT3gNlCZoLYgYnBfz6fl0YUzMwxOBkfH5fvAiDc/FgsBqfTCZvN\nhp07dwodaWJiAul0Ws6o0VgqNmYLah3Qx+NxRCIRFApbnUZZo6InhNNO0bbwXLAlurbJGonVbAra\nAa3n+KfOeGvQR9OYdcBeKds8a9RxX/nKVxCPx/Hkk0+WgZyzs7MyWLWjowNXX301vvCFLwgQCGz5\nEkA5tZN/594x83/77bfjpptuQk9PDy5cuIAHHnhAnplrSL8CKHXB4jM5nU40NDSITXc6nVhZWRFg\njh226LMwGOF7MstL+6T9pMbGRng8Hgm8eQ9m9JgJYGfUeDwug8UJusZiMQSDQZmBQloaqWikBOt6\nxu7ubgFuta6h/gEg9rVQKEit5MGDB8XJJ7hkNJZqYkkDy+Vy2LFjBxYXF3Hp0qWyjqlmsxk1NTUy\njmJ4eBiNjY3o7u7GLbfcgoMHD6KmpkYG8haLpWYhKysrWFlZwebmJpqbm+F2u1FdXQ2v1yvtuWOx\nmHQFm56eRiAQQCQSQTgcFtvFMoGNjQ1MTU1JNoWywHXlc7rdbinm93g88Pl8UvTP71tdXUVLS4u8\nO5s/sIYPgHQpW1paksCJQ1hHR0eFFUJbxf+///77y+p+NdVcg2GsuWNwRHohGz+Rynj48GHp8FZb\nWyuNJjictKmpSQLZSp+eF23xHwPCvS0zNzpi1U6H7lzEiweAyoGpbX6Gio6BgA44+Ht6o3QtAb9X\nF3NrehE3QDsGXHR+BzNJOvLVv897asWZzWbx85//HP39/YJ6U6hY7K7fixQXfoZONbNBnKLLf2dm\nQXfW0HU7wFYNEZss6C4owFY9B5HrTCaDlZUVfP3rX8ett96KX//614jH4/LdTz/9NF577TX09/dj\ncHAQ27ZtQ1NTkxTwce8pvLyY2aDTRsecPfgTiQT8fr/QODT6oekOVO6xWAxerxfRaBTd3d2oqanB\nxMQEotGoHPjx8XEpFD558iT6+/vluUgX4HrTcWRwplHJyvoTbbiZ7eFB1oX2pAwyiKzM9GneOQ84\nHe1wOIyuri68+OKL8jybm5syXJAGavv27YI8vfTSS9i/fz8effRR3HTTTcjn87j77rsRDocFVWpr\na8PRo0dlijHXmsEGhxOy/opXJYWKGVTSHVikTHnVnGW+H4OMynoXrp+uayOFkTMnKsEJHRQxCGUn\nH10boWuciPZWZp/4LgaDQepeSJ30+/3C99Z1FdRnmkvP+zFjzGJyZvD05/S+8zzzTLNzEmWJDgd1\nReXvU+/pgEbL2jvXW1/Ly8uiU6urq9Hd3Q2DwYCZmRn09PRgdna2LFNGaprb7ZYZZro+kk7N3Nyc\nIPGk/9A5oBzY7XasrKygvb1d0NLJyUns2LEDa2trCIVCAu643W6pySGNjtTJWCyGeDyO6upq+Hw+\nAYX8fj9WV1dhNBol62OxWLCysiJOM9sET09Pw+v1ltk0TSvT2UYdyGsbzzOmM9Q6ENF0YH1fHbxQ\nj9Kx1+APzwc/m06nsW/fPnR2dmJubk7OZ1VVFZ5++ml4vV4MDw/D5/NJBzDqYz5HJYrMZyLNlnqH\ngNapU6ewb98+mEwm7Nq1q+y88Xe1neBsovX1daEWxuNx6cxoNBqlbq+qqgqLi4uor68ve29mjrTu\n1IAb7X5jYyOi0ah0Z2SXN5vNJgyS1tZWbG5uoq+vT/S12+1Gf3+/2IlkMgmn0wmz2Sy0K+o8BjnU\nq+wsubS0JHtKQBmAUNwI0FRVVWFpaQkNDQ2YmJhAW1sbCoUCent7xbE3Gkt1MOFwWJoH6Zbt3Dd2\nl9O1l3rYOYMRBhfRaBSdnZ04cuSINAQhXW5xcRFms1naZLMhAjt/BYNBTE1NoaWlRToYsuU4O5HR\nLmpnnfsGbAV94XBY7GFNTQ127NiBUCgkdG+Xy4XDhw9j165dsFgscobT6TRaW1thsVgwOzuLnp4e\n2O12xONx7N27V1prk/5GJg6wNahWg/H0OQmusMaZPiKBDfpetNPcm87OTgSDQfh8PqytrWFmZgZ1\ndXUyLqEyQ/pmF/3T33e97YIbTbMiKq4Nsi58p3LRKKRWlpXZnErkmwqRh163wqMTYrPZpJVwpSOl\ngx0+h/4+OmiV3wWgTNFoVDqfz6Ourg4XL15Ef3+/ODz8Xq0A6VhRmfOd9fOxYJDFp5XUPCIGFEiN\nGFfSAMnHZ02BLqQmcvjqq69i//79uPvuu/F3f/d3wj298sorMTc3h1dffRUvvPACTKZSIfUHP/hB\nWRs6uDwYLPIk8kLDTqS0ra0Nt99+u6QyX375ZUHsXS6XPCvv09vbi8suuwwAcObMGWk9ubGxgd27\ndyMejyORSKCpqQmzs7NobW0FsFVv0t7ejrGxMQCQNpna6eR8Eo0Wco0pA1QeGonkvvF7SCPQ9SNa\nzvWh1lnNixcv4tprr0Umk8GhQ4fw1FNPCZ2OKCIpJi+99BLsdjsGBgZE2bEtKh0hKkDKHteTGSsG\ncPp5bDabKEkdMHMfGHjSCdBZEr6j3nu+ow6YqGT1v+nsw6FDh7C4uCjoJgNhyj0VrkbUtK7Q70O5\nz+Vy0thDn3eeK/7HlD+dUSLyPCv8DFF5ZgC0w0TEljQ16ga+L5+fz02nl1QmTQGIRqMiR9qppMzR\ncGmw553MzR++PB6PUCnOnDmDwcFBQeE3NjYwNzcn+0m7wBpJDX4weOHnSAlmli6fz8Pn84mjmk6n\nsXfvXgDApUuX0NzcDADYtWsXOjs7cezYMTQ0NIjTwzqDYDAow6DpyFH3Ly4uitx6vV5sbm4KXS6b\nzUo2lDVEfr8fa2truPbaawUsokPEswls0c80gFgZFOjgh0GUPk/aTuk6OmCLQv1m9EoyDWjXqF9Y\nm7m4uIjGxkb09fXh2LFjEqRee+21CIVCeOKJJzA3Nyf1U3fdddfv2HmeG13/xzUgY4BAx/XXXy9B\n1vT0tABXtCMENYFSNy+fz4disYhIJCJziTY3N9HY2IhsNis1EqSAcY4OUJpPt7y8DABlgZZmsdB+\nJxIJtLW1AYAATY2NjZibm0M0Gi0bvkhHn7NWrFYrfvOb32BwcFBaN7OpDbN+1DfRaFTaNNPBzefz\naGlpwfj4uOg6rZs7Oztx4sQJXH755fD7/cKkaGlpEftOOaUdoa4l3Yq1SrRb9J8YrG5sbEijIKDE\nkGEzkFwuV9ZFc2VlBa2trcjn8/B6vfB4PDKTiD5QY2OjyEM4HIbZbJb5UAzySF278cYbsbq6KhlL\n+g70w7LZrGQyKAO0DUajUahzACRQDIfDOHjwoNgAZnN6e3ulpIDnvqqqSsYLMOubSqXg8XjEv+S5\nZAZH+ymsq6GPyvMLQPw4dpHLZkszdWZnZ5FOp9HS0oJIJIK6ujr4fD6cO3cOO3bswMzMzFsCbvrv\nfwzD4G0X3HBzadApKESDzOatlph8SS64zp7w93WmhsqPyoQbQMeThl73nqcjp50AIsBUxPw7hZfo\nhy5+pDBQIWsuMREfvhP7wPMgkqvP79B8fd5PGxUqTl0ToWt/tNHhszPFyz3QipwoO79bozs8AMlk\nEktLS/jCF76AQ4cO4bbbbpM6D6vVir/5m78RRT4+Po6XX34ZY2NjyOdLHadI5eDe61oCn88nRftG\no1EyJ/Pz8/j3f/93oXUsLy/j1ltvFUfabrdLoMq1J6IBAB0dHTh79izy+dIU5NnZWZhMJhw9ehR9\nfX1YWloqGwyWSCTKJvbymShnes0ph2xQwHWtrLmiU0C5ZLZFBwGalsW91JkcfpYK+7vf/a50YqLi\n4kDY1tZWLC4u4m//9m9x/vx5ZDIZoapx7g2NsEYeaYj5LkRLU6lUmVPBQEgHrJQxBsKUT/1udBJ0\nYa4O/vkzfg+pWlwXymo+n0c4HC5ru53L5aRVLZ0BAGL46PBwL/W+VtLQNDda1zTx2Whgmanh83NO\nE89rZaZKBxrUe6zBI6qvn4POmubp67oa3qu6ulrknc8OlDuV2nBoiu4711tfqVQKwWAQly5dQltb\nG5aWllBdXY36+nop2n/qqadEntjYRaPtFotFMiekF7IYmfQvn88Hv9+P+fl5oZsxqzM6OiqzMVZX\nV3HhwgWZHcKAdnV1FX6/X7r6xWIxtLS04PXXXxewoampSfbc6/VicnJSOkEuLCxIwbXJZEIgEBCw\nhAg5ZYcBt2Yk8L5vFtDQvr0Zi0JnW7UO0WBKpXOj/QXeh3Rs7Zglk0k8+OCDuPbaa9HZ2Vk2g2z/\n/v2oqqrC+9//fsTjcQSDQYTDYbH3OtvGZ9KAoPY7CChEIhGcPHlSaEiPPvoovva1r5XVvPLddODG\n9/R4PAiHw2K/GTTEYjFpM069azAYymr3AIgu0fadOp6ZAw6IBSAz3Fj8XSiU2kSzfmNpaQnd3d0o\nFEpdxhggsT36xMQE+vv74ff7Reezux/rM4rFIk6ePClrQMSeLdPdbjfW1tbwvve9D9FoFPl8vqz7\nIJ9VMxf4J3WtlkE2kdG6zmQyiX2m3dnc3BQqJW0f62XYFTEUCsm8mfb2dqRSKbhcLrhcLszMzMDv\n9wtokclkMDw8DIfDgYWFBWE9RCIRqYOiLOVyOQm0IpEIvF6vzLIxm0vjNILBIDo6OuByuTA6OoqO\njg54vV4sLy+LvKXTaZnFZ7fbxWehzdH2hraYZ4ZAhaYI6kBCn23aL4J4BCx5lpl1LBZLlGvW8dls\nNiwuLortyuVyaG9v/712qvKs/zEMg7edFaNRpnND+ommYpjNZok8eVAZSGglCGxFebw3FQYdN24q\nlQIPj0ZidJpbOyZ8Pq1sdSBRSVfjZ3StDAVHp48DgQC6u7sFceBsAc1PZpChBVNnWZhdqaqqkoFy\nVHBcL9bc6CCPaAeFh1krBl90tEjTYTBmtVpx1VVX4frrr8czzzyDz3zmMzAajVhYWAAAPP744xgZ\nGUGxWMTg4CC+9KUv4f7775fuPkQ96QRbraWp3tXV1djc3JTaD7ZyXl5eLqtbqKmpQUtLSxkNJxQK\nCRLK7icTExO4cOECNjY2pKCRLU5ra2tlDgodRu5nc3OzpO2JoBJNI51J9/HXcsHnobxW7iUdaDri\nRL8oxxpx0kNDtcwYjaU6j9tvvx1NTU341Kc+Ba/Xi6qqKplxlE6nsbCwILMwiHgRxWMgrB0Eylku\nlxMDwQyCRmV1ZoHyTbllvQ2wlRXR78wAmf/pbKr+LM8QgQ6eZzoIPNf8DovFIs/s8/lQU1ODjo4O\nQYu8Xq84j0SgKNc8Q1ovMYPDdaeu0d9LvjHXg+vocrmEtkZqpdZdDLz1+aPx5X7ws7rDks7U0qAz\nvc9102eClwZ0KKM6Q/7O9fsvorZslbyysoK5uTnpzEgEfGBgQIbxkS4JlGSaLXPpaNLRYsYUKAVR\nly5dQk1Njejrqakpob9UV1fj3LlzaGxsxPz8vCDY7GzmdruRTCYRi8Wk6UAul0NPT49kadh6mt/B\ngICNaIxGo7RM54R2h8OBQCAgHZIqbRJQXlNDWa8MciinlG2d6dKgAZ9LBz20yfy51ls8ezrDSV3d\n3t6Oq666Ci+++CIee+wxye4aDCVaIeef1NXVYefOnXj3u9+NfD4Pt9stQAr1J4Ma7a+wBTtnv7E7\nl8lkQm1tLT72sY+JPQUg9CEi33yeaDQqepdzT9jZk12/NBXQYDAISs41Yl2kDsKYydm+fbtQx+lH\nmM2ljnqbm5uYmZlBKBQSm7y8vCz0ylAohGg0iuuuu05a+WazWaGpnzt3TrL4q6urUtfB9r7t7e1w\nOp3o7+8XfcVugAzKOQOmsgQA2PKbKgPlQqHUjl8Hy7S9PD+UM7bXZk0pfSVga6gx60Kz2Sy6u7ux\ntraGSCSCUCgkrZ/ZCZEzYigLfr9fajxDoRAuXbokrcdZkwlsNUki48ThcKCtrQ1Op1NmyDQ3N+Oy\nyy7D3r17EQ6HMTExgW3btgmtkO/J2TbRaFRqRLVdpZ0yGo0ymFdnSa1Wq+yHzvbpzzAYpw2jzdHM\nH2aFaE8pf3a7HclkUmra6Dfl83l0dnZK8yVetFNvdv1/l7nRWQcuXKXxrkSuuXk6hV1ppLUTSaXH\nRddZG25OPB4v+14iwRqF558MgLRjpZ+DHU6YetZZGjqY+gDTES8WixJgsS5FK8VcbmsoYD6fl/Sm\nrtdggEPlmkqlZNCgps1xbRhM0sDx+7nWdKboDBJlNBhKA8fuu+8+TE5O4rnnnkMoFMLU1BScTid+\n/OMfw263o6+vD4ODg9i+fTtcLhc8Ho8Yf6LxXKeNjQ0JKOx2u6DvNCCf/exnpb30sWPHhI9MZ66m\npgbZbBYul0vqavL5vNRCcKqxx+NBNBqVacIcWMXAj7SR9vZ2HDt2DAaDAdu3b8e5c+cEoaEi5bpz\nj8hP5n5pZV1J09DOPACpM2LmkIgJg3DdscxkKk0jnp6eRn19PaxWK9797ndj165dwrO/7777xAEL\nh8PSTczn80mgyrojno2mpiahSGlaF+Wb76A7wPFikK+DEzrrWn5pjHk/njmdYaxEdIko6nPPwkam\n4mlIKvnCBE90rRoBAA7tJaCin0t38dPBAM8y0XK27QUgssFsM51Prg91kc4o67Q776tRWKPRKDQA\n6h7qRQZ7RNcZMGnni7KmHUSdLfxDRuOdq9ROXqPHPKs2mw3hcBg2mw3Nzc0S5NbX1yMSiUgXJGCr\nQybvYzQapd6K+x6Px9HR0YFIJCKzWYLBIIxGIxobG/Ef//EfMJvNGBsbw9LSEgYHB5HJZMTJpBOS\ny+UkE+PxeKSNqgYKjh8/jn379qGxsRFjY2Po7OyUjFJTUxOWlpakBod1FVp+NHioATcCkvwcwQ+e\nYT4n/007+jzfb5bR0XSVShCTz0WgSYMyJpMJV199NXbs2IGFhQWk02mZvXHvvfdicHAQ+/btQ3Nz\nswASrLvhd/PsakAQ2MqkUy5yudLsN9Y4sYU4gw46fGRPkKZEnaVHILD9MjNEPNPa9gClwJugot/v\nF4oiwVy+g6a9vvHGGwKCAaWSgLq6OnHgOdsIKA29ZJ2O0WjEtm3b0Nvbi1OnTkkH12g0ivPnz2N9\nfR2XXXaZ+AsclMnsgtFYop95vV5UV1fDYrHgxIkTSCQSqK2tlQARgIBOlCHuJQEcyoumTVEetH+j\nszpcf/6M8kWWCVBiaPDckfLG71xaWpI6E2ZVC4WCtHN3OByIxWKIRCKyX42NjbjsssvkzHDdtXxr\nIJ/PXygUUFNTg4GBAUQiEZw4cQITExPCICIl+eLFi7j88svFnmh2AYNMbQOrqqqkBEQ3PdK2Vvvj\nun5d+y7MrAFb3UDZTZE+FYHoZDKJVCqFuro6YVOsrq6ira1NZg7yvpX2SGffft/1tgtugK3F5AHn\nouuUmnbwqWwqi5m1UFciR5URog5stMLe2NgoQ4i5sFTW3OzKdDmfUafqqay04GokiM980003YWpq\nCo2NjWW1Okz18btpHKhQKVy8J2t1eH8aTKL/mhpXSSNiMEUniUiHrv+hIeKsmF/96lc4dOiQKLuH\nHnoIzzzzDIxGI/70T/8UZ8+exaVLl3D+/HmYTCZceeWVMltGUw/4rtp51weHa/yP//iPsj/19fW4\n44478PrrrwvvdWVlRdoihkIhofl0d3djZmZG1o77MzExgZqaGkE0OXzr7NmzCAQCOHfunCA1Dz30\nUBmdjLK1uroq0361rGoKBuWN70GlwSCWRo8onnZgiUBVIqKbm5v43ve+B5PJhP7+frhcLnzwgx+U\nLJjZbMaf//mfY2RkBD/60Y8EQWMrUN6b61ldXS2D2Jjd0XLENctkMqIsWQBPo861Yb2U/l2iP/x9\nGjCdzaEzzvXRDj/Pvs5IMhjL5XKIxWLS1Y1ILM87nT0CG6QIGY0lyhhpCHSy+Ltcaz431416h3vH\ntSFfmgZ3c3NTugBRBqjfKEOVxZq8uF4aqaVjxSw2Oc6VAQvvXfkeGtyg3qTB0jTXd67fvYiSs3A6\nGo2ipqYGtT0ZkwAAIABJREFUU1NT8Hq9ApBxIGwwGEQul5O6BQasRqNR9AWdT2b++B0OhwNra2sw\nm0udL6uqqlBbWyt7lMvlcP78eVgsFqGu0DboOjEO/5ybmxPnJR6Pw2wuNTLo7u6WGs19+/ZhY2MD\na2trQoszGrda9ZrNZgwMDEg9pM64aNniWmkgQNtDoJyGorPRtMma3sY/AfyOvdBOayW1hefUaDRi\ncnISLS0t8Hq9qK2txcTEBI4cOYLh4WF8+ctfxrlz5/Db3/4WL730Eurq6nD11Vejrq6uLDAAyn0G\nBj7a/+D18ssvY2ZmBm63G36/H3v37kUoFCobdOlwOCRLw/Wrq6uTQJe2FoDUQzIoWl9fh8vlQjQa\nlfbLZKY8++yz8uy6fnN8fBzDw8N49dVXpe08HcyNjQ24XC5sbm4iEokA2OpuaTab0draijNnzuDq\nq6+GwWDAjh07YDSWamFIa25ra4PP55MOXfQ9QqEQPvvZzwr4WFVVha6uLrFrBoMBAwMDiMViOHbs\nmPhIrBejnNGu8pxUZiUoR7Qjmmnh8XikbT7fSb+zBpUIWqTTaXR3d+P48ePyPalUCr29vUgkEojF\nYtIgh11BOdNncXERVqsVLS0tkkndv3+/sF5YQ3XhwgWxR2azWZgqsVhMGAZ1dXUwm0utlnft2iXB\n4sjIiNhN7iWH7/b19cFgMEhzGrbAps/Hc8H3ymQyshaUd20raNfJnGGzIp3V4fmj/aOM0rbqRiSZ\nTAa1tbWIx+OSCdZ2SoOmlTrl911vy+CGEbhGuICtQEIjNsCW4efnuaA6sqQjoxUm762jw8oWxBS2\nyuwK/9Nce6CknL1eryBBDNQoDEyF6vvroXlU/FQq/A6+nw7INIWA78CDwYPF32PaVT+3Tlczcgcg\nCBIzP0xXs6sMn0Ovg8PhwPz8PD72sY8JPY2zPywWC7761a/CaDRicXERo6OjOH/+vGRYWB9CZKKy\npkrXHPB9VlZWJOVLJOGJJ55AU1OTOO1UBES56ahfunRJJm3TweSE75dffhnBYFCoF9oIk37H2TcN\nDQ3CKU0kEuLkPvroo2VpXV30qOlTfEf9We4VAHFyNILJzIPO+hmNpVaut912G/7qr/4K2WwW1113\nnSCryWRSkOTXXnsNd955J+bm5uB2u7G8vCxGBoC0BuW7aFSUAQGLBoFS333KImWISJCmXFFJ6cYB\nlDudldC8c60k9VmuzHwx20FHn92EbDab1C3wXLCzTiqVKptXox0yOp4aUadC52cY5NC4mkwmofDw\nPJpMJqH78Vzxd2hUGVTx/NrtdqkBYgtY0gb1DADuO5F/3tvlconB1INsqXNYQ1bp+Gn6wTuZmz98\nMXvLzG8+Xyr8p/xPTk5KwMKzEAgEpF7CYDCgtrZWOtrRdiWTSdTV1QlSz3knLOKnHnvjjTdE/pkB\nbW5uRigUgsfjQSaTwdramlCk2OWQdZAbGxv49Kc/jQcffBA+nw+1tbVwOp0Ih8Po6enB9PS02AMW\nrM/MzMiZ6OjoALA1SFRnMzWIU5nd1D8neFBZp6PPtw6GAJTZddo+6uhKJJn/X6krYrEYHn/8cfT3\n90t2jfTA3bt3Y8+ePdIul9PYAUgWtFgsCkrO81IZfPH7k8kkPB4PhoeHJat74cIFobgxeKW+0hlr\nzkfRDjfP9eTkJILBIILBoGSoGbzMzs6ioaEBDocDu3fvlnbg3Pd8Po/+/n585zvfkc54wWAQJ06c\nQFNTE5aXl9HR0VHWSCmVSom8csbTkSNHYLFYcO2118LlcqGvr0/8nunpaWnQ09nZiXA4jKqqUje6\ndDqNr3zlKwBK7agJXNLmOBwOhMNhDA0NSZMUngfuqWbsaP3JNbdaraipqZHzWldXBwAiM1VVVUL5\nNxgM0hKdmSw+E/+N628ymbCwsIDm5mYBvnSWiRkk+oGZTEYAgebmZiSTSQFa6QtaLBbEYjEMDAyI\nvdzY2JDs2NzcHCwWi8wLoj/BRgXHjx+H0WgU+xMOhxGNRsX3YuManiXWWWlQluAjAPELmVSg/6XP\nN2ulWCdE32Rzc1MCOKPRKEwlZrS5Jnv27MHCwgKSySQaGxvLZuusr68LQ6YysPm/sU1vu+CGC64d\nQQo9AwkdzdGZoGAXClvdHPjzSqRXI5U6W6ENPpEYOu/8DJWqzgrp79EZEVJudLpdNwTQ3VVYR2Qy\nmTAyMlKWddF0MD43HVyNVmgnhU4TUWO2RdQpdG1MSAcjtYuOJiN57eizEwcRbH7Hu9/9bpw8eRJP\nPPEEnnzySXz/+9+Hx+PBxsYGfvazn+Gaa66RLiE333wzjh07hpGRkbJglnQEnfYnPU7v5Z49e5DL\n5fDGG29IcMvJuESI+vr6YLVa5e9jY2OizHTA+Oyzz8JkMqGurg5NTU04ePCgpIrpIBgMBgl8aUhj\nsZgEYqurq2UOJfcIgChK7mPlAaVc6YuyzAwNFVcymQSAsiCBiM1dd92FH/zgB2htbcVll10mSuZn\nP/uZtMuura3F6dOnMTs7C4PBgLNnz6Ljf7mvlBf2oKeDTzmxWCz48Ic/jEcffVTeUwegOgig3PJi\nEEGHX2c1uV46iOP+VAY8XCfthGtqXrFYFJSbBZ2FQkEmq1NR06HRGanK4F9n1KiDKDOVASnXgQCA\nTs+Tx60dFD2IT4M1PMv5fF5aVFO+OZSTGSEG7aRQkOZEXaMBFa13dA2PzoppQ/LO9fuvpaUleDwe\nzM/Po729HVVVVVLgz0Jq7mGxWJRCZNJBWENI3aBpG7FYTD5DPURKj8lkkoDHYDCgtbVVsuek+uiA\nlgMJSUP2eDzS3j0SiaCxsRG7du3CpUuXhCZstVrR2toqHbAuXbqEQCCAYDCIlpYWdHV1weFwYGVl\nRc4MUG4D3yywqAw4+DsaKOPZovzy0vZPAwj8HZ5FzXSgnefP+Pne3l5cuHABr732Gmw2G2677TbU\n1NQgn8/j0qVLaGpqgsViQWtrqxTKMyjVdpbPogMq6i+eK3bSGhkZkWJtUpdYa+H3+8W/WVtbw/Ly\nsrArtM757W9/K0X9Pp8PHR0dErTQsTQYDBgaGioLDtfX1+V+1IfM1hWLRcRiMZhMJuzdu1f0HKlz\n1BXFYonFQmoa94nvQL/illtugcViwXPPPYdisYje3l4YjUaRS4/Hg8985jN46aWXpNaVOmpiYkIy\nnVarFdFoVMoDQqEQdu/eXSY/rD+qzCzU1NTgwIEDOHnypNSKaVuhAR0GlcAW0KPr4Li2NptNmucw\nW2M0llqlLy8vIxQKoampSRrA0Neqra0VsIwDSb1eLwBIQw8CEblcqfEN/R+zudRpjWAHAKFcM7DT\nDUhaW1tx7tw5+Hw+rKysyHmmbGhZYkdTygmbS2gwk3WAGrjQwDzPnZ6pSACtqqpKBp273W6RcwLE\nNpsNXV1dQvt3Op2IRqNS+8xZX9ouab/p/8vMjUaz6dhq5Eanq6iwdBRceR+tfDTyT4Hgz3UqTFO0\nALypU6rvz8NFx4EbSKeNzgONF9FZKhvSW/L5PLq7u3Hq1Cnp2qKVId+BQspomodBF5AVCgWh1hAh\nJsJHx5IIPNcwHA7L4Sf6oAM2YMsYEdWhM2u1WvH5z38eTqcTFy9exDPPPCNTk202Gx544AE88sgj\nsFgsaG5uRl9fH5qamtDQ0IAzZ86gubkZxWJRjLl2enV6slgscV3feOMNhEIhXH755di5c6dMpH/2\n2WdRVVUlishqtcr39Pf3i0JKJBIyeJTKhdSAQqEg1Dk65Kurq1LLRFmjjHK/uVa6VkZnv5xOJxKJ\nRNlMGwaQpCxqo68NOFPklAM6FXRmq6urEYvFsHv3bmSzWTzxxBPI5XJobGzEysoKJicnYbfb0dLS\nAqfTKWngzs5OXLx4EUajEV6vF93d3WJAdXaJclxfXy8ypPnsRH54dnRLaGZNKOOaRsnfT6fTcobo\nJGgaBZ+D8sYzwDWkPmCRNjNCBAn4HZreyD2hQwJAziX1RDQaleel/OnAgO9BI6KRahp//dwEMfi8\nb+bIVVdXi8zorK7JZBKKH8+53W7H6uoqTCaTGE6dRSOgQgRWO5kaoNEO2jvXH75sNptQtrLZLGZm\nZtDW1iaySX1Po51Op4WmQx3KgD+dTktLVsp+Op0WRLehoUGy4Cwk39jYkNkjzJ42NzdjbW2tbFCo\n1WqVOsJCoVRs7XK5EIlE8Oqrr2JoaAiTk5Niy6qrqzE+Pg673Y7BwUGMj4/DZDLh7NmzMnF9bW0N\nQ0NDWFhYQHt7e9lZ0LLEc0A7qmWdThWwBTryzFCPAFvMBV5Go1EAF95T30v7Cfq7eS+j0YihoSHs\n2bMH8XgcMzMzogdMJhP+7d/+DUNDQ8hkMuju7obP55OhjaSDAVu1r9QVlc4fUNJLCwsLePHFF3Hr\nrbeioaEBbrcb6XQak5OTck75WVLNOHNIo+PFYhHbt28XoILvvLy8XBYQMiNMYEz7NZqGDgB9fX3o\n7e3F2NgYEokETp06herqavzJn/wJHn74YWzbtk3ows3NzVhYWMDa2pq0nTYYDHC73ZidnUUsFkM6\nncaOHTvQ1dUlwym7u7uRSCQQCARgtVrxrne9C9lsFr29vSgUCiJ7TqcTqVRKaj45vJKy7vV6JUPC\n2kqTySTdRXVm0GQySWG+9mWA8ppLgl6k3JHeC2wxfXiGCAzabDYsLy/D6/UilUoJ9Uu3T04mk6iq\nqpJap2g0ikKhIE0CSGHTADa7mxkMBtEHWubdbncZvZhn3Gaz4ZZbbsF//ud/StMavk99fb34F1wz\n1qRWnhGuh5ZnZsoqEwA8e/QpNaDJM8nGBARVampqsLi4CIfDgdHRUezduxcmk0mybEZjqRZndnYW\ngUCgjKGlbT2vPyaD87brlqYLbnWQA2xlK94s+6IVmUZSecipMOm8U3B5X406vdlCUoEAWxkPHcFq\nNIcOrQ6etKNIJ4jCo9HoD33oQzh06BCsViv8fj8KhYIYLBau8fkikQiy2SzW1tawsbFRxjnWxf6c\nwK6fl4ea6DbRPFJveC+bzQaXyyXrq4cMUmH4/X6YzWbcc889WFlZQUdHBz73uc9hc3MT7e3tWFhY\nwAc/+EF0/C+V4fTp03jsscdgs9lk8BfnKbDQjMqYfE69r2w9GAgEMDs7i2eeeQY/+clP8Nhjj8l8\nkd7eXhmGVygUcObMGYyOjmJ6ehqzs7NYXFxEKBSSKcx0fgOBAOrq6oRHTAeTA9V0DYYOfnUGpdJZ\n5XwLTfXif5rmRwVSiXTq+hNmH5g942fItc7lclhYWBDHPhqNwmQyiSOUyWSEutLZ2Yndu3ej438p\nCCxCTSQSCIVCSCQSKBRKDS74uxr15xkg8sNnpsywOw4Dbf6Xy+WEHqGBBh0IUE4rzzKAMieK9TsA\npJECQQGv1ysGn04nAw7uidVqhdPplHUlPUHvHc+/zsoSJaWOSiQSZZRWnWFlgTA7ZfGeRE1Zd0RK\nk9Z7dGb5njT+Pp9P6BrMqjocDuGS8/tpwBnUMfCmPtOOEmWwMov4zvW7VzQala6DnLhN2QgGg1hb\nW5M99Pv9ALaceLICGHgQFU6lUjJrhlQ1r9crjlwsFpPZRay/0YEERwYwwLVYLAgGg1LXwRbUHCa6\nsbEhmVun0wm73Y5EIiH6dXl5GblcDn19feIAMRs0PDyM7du3C0pNO0v7rVkNHI+gZ4Lx7NCm83kr\nbXplzRodQDqyGuAgWEdAj4Af70Vqzquvvor19XU4nU4MDQ2hUChIy9277roLbrcbZrMZzzzzDJ56\n6qkyloUGXjRdTNNrdbCXyWRw/fXXY3V1FRMTExgZGcGFCxfEN6ivrxebAZQ6btEmUSaYlaN+YVG4\nzWaD3+8XH4J6lAER5UL7FzrQtNlsiEQiYreuvPJK7N+/X1qBk95aKBRw6dIlCRY4tJGd9gjYbd++\nHaurq1hdXcXAwACamppw4cIFdHR0SNtjTqEvFApSU0jwxWgsFddzjXk2amtrEQgExA8hqMrmL1x7\nBvB8Jw3YcG8YdFG3VldXw+FwwO12C6hAO0Xgk/KTyWTQ2NgIn8+H1dVVmM1mGbHAmUNcm0Kh1DiA\nWdRt27ahubkZBoMBhw4dkhqjYrEog0+j0agEr7RVFotFwGvaAZYU0JctFAq49tprpamBxWLB5OQk\nvF6vANasC+egbcot78H1Zo0sAAkcC4UCksmkMAE0CEFfnPaJ546Am8/ng9PpRDwel8ZR27dvRzAY\nFN+GurG2tla67nV2dko3N9rs/9vrbZe5IVKu0RAKCFA+0Ec7hHSSDAaDFHlyYRjYUMg1mqEpHMBW\n8RT/XVNe+P26boJ/UpnRCdOF+Nx4rcz5e7rVaz6fx3e/+108+eSTeOSRR6RFLdOPiUQCNTU18jxE\n8zSdRgdNdH4ymQxWV1fFCFVVlWYo0HjwvXVdDteOSBAPAbMWRJRNplLh7Pj4OF544QXce++92Ldv\nH/bs2QOz2SwH7LOf/axQhaanp/HCCy+IUuQz04iQK0sjxT2noTMajfjIRz4ik7jJ+06lUqivr0c+\nnxdqgdFoxOnTp8WxLBQKophoELjXyWQSExMTcLlcgqSmUilByHnIdOaQASfrJLQzrmkMVAikmzCr\nxLXnOtAx12iJNqC8F2cPEZmx2+34yEc+ghMnTuDOO++U3vjkAHOWhp65MTExIfVhnJwcDodl3X75\ny1/iPe95jzwn78c10PViTqdTEFW2uSQaxhQ+pyhXVVWJogYgKejZ2dky48vsBS+eU64LlTLlo7u7\nW9bW4/FgdXVVPsfsImVKB1aUCz4zMyN8Z50V1ogtMzHcL9IMSKfjczGA0G1Cq6urZTgigz1muug0\nMVvHPSwWi9KmlQaeZ0a3f9XZXV6UPd3mnbqNgTuAsn9/53rrq6OjA36/H9FoVDKx8/Pz6Ovrk8DD\n5XIhkUiUFdKyPbnRaMTHP/5x3H///aL74vE4fD6f6BzWe+oZGyzYzufzWFxchM1mg9vtlnkjDJyT\nySRaW1sRCASk7ovZUSK4a2tr2Lt3L06cOCHDKtva2rC2toZkMimU20QigcsvvxzBYFA6uD3xxBP4\n3Oc+h4mJCaF8asod0W7qKqPRKDJcCQDo7LUGgIxGo9hQnWnUOkA7Z/ycBih5vglSra2t4fvf/z4M\nBgOamppQV1cHg8EgLbwHBwexc+dOac7B+UL8Ho1ga3BSg7AaKN25c6c44DyjpBgWCgUJXI3GUh2p\n9nMq61uB0vnkfhKQ4bvRfmidST+HAafWl2xhzMwfgY+6ujq0t7dLkTpByY2NDfT29iKbzaKrqwvx\neFwK8TmwdGNjA+fOnZN6j76+PoyMjCAQCCCRSMjg1OXl/8Peu8dGnl51n98qV9kul+0ql8tVvrbd\ndvd0u7unJz3XZMgwZGYzLEEJC4hdSARL9g2KiAirICEtCPgLITYKIOWPXSGhzXKHV4EQlExCEnIj\nTJKeW6Z7unt67PalfSu7XOVyle+uy/5R/Tl1qqZzgReiybvzkyzbdfldnuc85/I933OerKanp5to\nyPz2We1yuWx7qAAe4KTzndXVVWuMI8lamjMOgFK1Ws06tREkVSoVFQoFxeNxXblyxTJnw8PDTTWR\nUr0+iH2gyKSkUinNzs5aoxiAxqWlJcXjcaMNswFoOBzW0NCQySd0VPwyKHDIWit7Ajt1dHRkbdkl\n2f44UOzYSgQWCwyFSqVidgr2jqfPS7JACL8Eu0VQD4gB0O737WIdUIeIzJPp9vorHo9bdhI57ezs\nVDqd1sDAgDUFIsPFOTg84Hm343UX3IRCISuoxiGKRqNmwL0iZNHieDAhvlUdE8ThF/7d0ugIE84L\ng+opKQQpnsOI0PE65/cIt78H7sNnJUAORkZGFIvFLCPCs1EgzmKl8B8KEWPn0RvOz1jxHKFQyMbJ\n09kwvigS392J7APjBLWpWCzq8PBQ73znOzUzM6OZmRndunVL733ve2235K9+9au6ePGiEomELl68\nqIsXL+oP/uAPNDQ0ZBxUMkOMBYsTxxSFjlIfHBy0vv6eBlgoFKxmJBKJ6KmnntI3v/lNC9iouwkG\n6xQ1evcHAnVuL8YIBAPDLTUyi2TmuDYUNy8XyBCOJcGIpy94J56AyyP3OL8Edygk5g75Zj+NX/ql\nX2pq2wyiinMBpxd0Lh6Pq7Oz05C89vZ2jY6OKhgM6qmnnrIAHxmORCLWbS6VSlmQ4mtcKCakranf\n3I4fOux52fPryBu61nXC+vSKjbXX1dWl+fl5pVIpHR/X261SmI1cEHDADfZACj+MNTLnAy3fMMA7\nW1DGkFevW1h/5XLZsp78eOXuaWI02vAGVqo7IXTBA9FHTplvgnHGiH2hkOPWcfRj/72k/P//fpw8\neVLPP/+8GeQ3velNeuKJJzQ3N6cbN25oZGREy8vLBr5QRyPJiskzmYwhsZVKxeg+ODccUN+osYrF\nYtblDOpTJpPR+fPnrbFJNBrVwcGBMpmM+vv7tbm5adSfg4MDK3SX6sXW8OJB2KnPYV3S/bGrq0u5\nXE5LS0tWqO4bIqDDPdsCPUmQHwwGmxwoXxPRSn1BLr0O8kg694eN94E9a8o36ahUKvr1X/915fN5\nfetb39L4+LjVZu7u7lpWOxKJWP3Ut771LfX09Jgt9hlO7pX75DPYmmAwaGuV52BNkmnjnkdGRrS5\nuWm63oNZfr83AhNsPHLlAVzG0YM4zCufSSQSyuVyam9v18bGhpLJpEZHR/Xiiy9ax1Gc4EQioaWl\nJfX09Fggsb29rWQyaaDM2tqa0cBv376tyclJzczMmI0eHh62vUwIbLyu8YEONqVQKJjseIYD1DNJ\nlhFhvqHk0m2OFtP4XYwbwSPtzR999FG7FoCu96f4HnVs2DDAAknGOoE1AnuC9VEsFi0IpZlPtVpv\nyDQ+Pq61tTVricxYeNDZ2xMPkh0eHurcuXO6fv26BTKZTKap2QFBOkwZ9mBqtbM+c+afG9YA4Ak+\n6OTkpMmPp5BGo1FFo1HzLwAv0Wf7+/tKJBJNtbYf+9jHdPbsWa2vr5tM876naHOv3+l43QU3nlrF\npLN4cPgQPAy1d0zK5UY7RZxkDibNFz97NNjTgDzFpjVrwXe4R48QoMw98k5AJjWCK59B8oFTW1ub\nUU3OnDljqUAO7sVzRhkzHF8cLElGLZJkDjxZjFqtZpxar6BBckGleS5ffB2Px63xAIj4Rz7yES0u\nLmp5eVmbm5uG5ESjUb3wwgv60pe+pL6+PstGnTx5Upubm+rq6rLuM3SzOj4+tk5oPqDAWYbHm8lk\ntLW1pa2tLY2NjekjH/mIyuWyZWGee+45Pf3007pw4YKeeeYZxeNxbW9vK51ON2UvMpmMFeDxzPv7\n+xYMIS8YFxTU3t6evU9LZOhjzJ133jm8M0tQwzijbHwAw7zTftHLbaVS0W/+5m/qK1/5iiKRSNMm\nkswzGZT9/X0r0gQlhp7FeW/duqXe3l6jDXpaVDQa1czMjMLhsDY2NmxNQZNDNqk980YfnrHffI6C\nTzbb9GPTaqip82Jt+6wewQB6IhKJKBqNan9/X2tra+Yc7u7uNhXFsoYJCAk0yI4g78yld66YFxzB\nnZ0dQ7BYf3wHmgMbm0JDY25wZjyAgGz4rAwyuLu7a8WavmU4MsVGaegJX1/nf5PJaQ3E3ji+8zE/\nPy+p7qBShzA3N6dbt24pnU6rVqtpYmJCBwcHxr3HScKwP/3000aLhJcfDNY7HxLEkPUJBoNaW1tT\nd3e3SqWSwuGwTpw4YbJAC/tIJKJr164Z1fL4+Ng2HGQzyVAopGKxqNHRUbvOwcFBE9cdJBi9Ra2B\nzyKRufRsAr+GfUbDgzrItXe0Pf2HtYPTLqnJsWHteYDOB+ie1o595P1AIKC3vvWtpguhrALsZTIZ\nLS4ummPqbR3BJAi2BxI8MACyDXAFXZTW9L29vfqhH/ohy+YWCgXlcjnb3T6fzxuiT5Mc6E87OzuW\nzWUNMwboDfSXtx2+4QB6M5/PWxYlFospm80ql8vZ/jPo1sPDQxUKBaPQkb1LJpPq6enR4uKitVfm\nM9Cwent7tbq6qu7ubu3s7Oitb32rba7tAxap0aSgtd7QNxzybIhCoWA6DoZJIBCwQDSXy6mtrc1s\nNPqd7LsvIfBAOIAxAZHX0XRAlBrbZEDfY3+We+65R88995zp5+HhYbsXshzYRLKz5XJZ+Xxe+Xze\nOiPSrpn7JGMDCB4M1uvPbt++bb7c7du3NT09raOjI2sAQRByeHhoGV6SBqw1AAkCtrW1NW1vb2t8\nfNyYHul02vwIMisAlL29vdrc3JTUsFPhcL0jHS3zqfUFLD4+PtbCwoL6+/sNAJ2enrbgyWfuYDn8\nW47vGtwEAoH/R9KPS9qo1Wr33nktIelvJY1LWpD0P9dqtcKd935D0v8mqSLpV2u12ufuvP6ApP9X\nUqekp2u12v9+t+uhfEm9MghMsnewGUgWoVeanl7lnUsf4ZMh8ogH58KJ8dQVFoSkJqXtz+k7vBFE\neQeN5/OIPIsN5/fZZ59Vf3+/PvWpT+ld73qXLXQCBYTWU918UMiCqdXqLYoRcJQcvNNWVAfF4Xdi\nPjg4sE5dPqBEeYI8TU1N6erVq1pYWNCJEycUiURssZ48edIE3rfP3NzcNE45PE3um25DgUDAWpsy\n7g888IDm5+e1u7urSCRiSv3g4EAf/vCHtby83FQHs7W1Zb3mUaooPWQpFoupXC7rwoULevHFF40b\nznWhDzIGBDI+fevbaXvHHHnAmfboPDLsla2fR2/wkV8fWBNM/83f/I1Onz5tSAkBQzwet3Ena8G6\nIABgjSGTvEenr/7+fkM9+X9/f/81iCvyDxrb1tZmQSuUHBQe36tWq0YP8A6IX3OsM19n1Dqmkpqo\nEJy7s7NTmUxGyWTSkCfvBHANv3Er49Xd3a1yuWzBK+uGucfgkInyqGw4XG+56fd5wAD7oIzD6wT0\nAPJXyHZ/AAAgAElEQVRPdslnmv0O6ARXXpdB5WBuPNXHU1y4hgddfhCP77edSiQSGhsb0/PPP6/9\n/X1tbW1pbm5OY2NjSiQSunHjho6Pj41i5us+oars7e0ZqkrBdKFQsCAoGKzvK3P58mUFAvVmHp6S\nwsaIOB6rq6smn+zvlUqltLa2ZgAG6zaZTKpSqejVV1/V8PCwNcQolUqWtaaV/vr6utHWcPprtZrW\n19fV3t6u27dv68SJE5IatG5kttUG+oyKD4aw8bxP4ODBBK8fAHqQZUA2QCCfOWINSXX67ObmpoEc\n2I/9/X3FYjFrk+tRYagxgHi+c6fP0GIj7shREwDIeHBPL730kmXD0ANsWo2vg03GmUafVSoVDQwM\nWM0X55Qa699n9Vn3HD5LDugC8DkzM6Pe3l5tbW2Zs4pPdXx8bA7q1taWBbxQH3t7e5XJZKxWgsxL\nqVTS1taWTp8+bYwBbDABB4EE2QiCKg9k4UMxFh7k9QEfGQr8Hu8r8hs7zTpjzQECMP4cBKKA5729\nvapWq5aJYXxTqZRmZmY0MTGhnZ0dVSoVzczMNNW/8pNKpezcrJNz585pcXHRuqB53xIZ8+N1fHxs\n9nl1dVW9vb368pe/rEqlokcffdSAEuxHqVRqyuAT5CwvLzexKKhD8rYSAIDMDzQ2upP29/fbtgTI\n4Nramm3kns1m9dBDD6labXQmff755/Xkk082rZmjoyNjBTHH+Gr/luN7aSjwMUn/Y8tr/4ekz9dq\ntXsk/fOd/xUIBM5J+l8knbvznf8r0LDg/7ek/1Kr1U5LOh0IBFrPKUmWeUBgeDgWOg4SSg9l1ioE\n3nnyyhHHyytKH0Sx4Fk8PsiS1ORU+utIzZuGemecc/n9JjyS7xXi4OCgPvaxj9m953K511Cb2tvr\nvctRjqFQyFAoOJU8F7vA4sChgDFSBGe052NB8z77uHjjRAYF5UkNhSTdf//9SqVSyufz+upXv6pg\nMKjh4WE9/PDDevDBB/WWt7zFDMry8rK+8Y1v6MMf/rBmZmZ048YNC6RwNFGUKDkWNzUiOBljY2Oa\nmprSxYsXjWZw7do13bx50xAVn4XZ2dmxZ15ZWbHX19bWdObMmabMoUdwWtEjFvzdakGk5k0pMUwc\nrc6wV+Jelvks58NBJlirVutFek888YTi8biNIShJV1eXBgYGmsa1p6fH+N1cA6V58uRJSQ1DiaPk\nMzJSvatXLBazokGCEoogka9QqN79BpSa8UD2oPixhlqzIzg6PD/35p0g7hX+P0Z9f39fk5OTdk/c\nA3SR7u5uxWIxxWIxDQ4OamBgwNA2Nk5jnxzuzdeboUcYFxyASqWijY0NW584YsyXlw2C1UAgYF38\nMECME7pDkiGNUiPAy+VyymazBmyQPcC5isVi1hHKgz6ezuMpaz+Ax/fVTt1zzz26efOm6aJgsL5T\neyKRUHd3t86ePavJyUljHfAZ5hH9T+aAjHUwGLTCcboqgSLv7u4qn89rdXVVh4eHunr1qqH8L730\nktXKBIP1+o2DgwNrOuCZD9SXlMtlzczMKBCod70qFAqKRqOanp42+SNjAM0Fmzs5OamXX365CShk\nzXowhkyVfx0HyutIZJH14uUU/Q8AhM2XZJ/F0eLwa4t7BqCT6sEpNNLV1VWzj/39/UokEkomkxZY\nUHvzwgsvaHt72zpnemCQe+c+0ePYbWqjQLH7+voseMxms9rc3LQAwmdhAETC4bBlDQKB+oaMyWTS\ndCX1DqzdViCXe/G6xzcUwG84deqU7r33XqPH1Wo1a+FbrVYNuKRuhOwF9p8mSOVyWclkUslkUvfe\ne69GR0ctABoeHrZgEd+DZyS76MfVZ/CkRqMpX5vLGGCzCbIl2fjTNMMHyr5WJBQKNdHJJDXJHqyR\nWq1mpQBk9KB50e4d4Hx4eNjmK5PJWGmBbxqAT0oZQjweN1/NZ0B4rbe31+qC+/r6FIvF1N/fr9On\nTysej1tQPTc3Z3YOSvzBwYFyuZxlYMvlstXBRCIRA5JhTLGOAMUB7b/4xS/q6tWrVm8FvZ16J6ne\nHAM7PTs7q66uLn3iE5/Q5z//ebPFP/mTP6larb6n0/7+vu677z6dP39e8Xjc7KDfuP7fcnzXb9Rq\ntX8JBAITLS+/S9Ljd/7+U0lfVt1w/ISkv67VaseSFgKBwKykRwKBwKKknlqtdvnOd/5M0v8k6bN3\nuybCKjUcCFKqoKqek8rCRVmi/HwA4xFKlJy/DgqJozXa90iOr4fwjqynCaF0WWBSAyXgepwXx4yg\nhZaDly5d0v7+ftPGe5FIRIVCQbFYzBx9Il4MJYsfZIlN2lDgKEiccn6TMi2Xy0okEsYVBbXGIENl\nCQaD+sQnPqGZmRml02kVi0UT6FAopJGREXV1demVV17R9evXbbExJ4lEwjiuf/qnf6p3vvOdymQy\nRtkhfY3z6juM0HoxmUxqfHxcq6uruu+++yTVHb5isai3vOUthiKAcIOQnjt3Trdu3TJKCYHhxsaG\n1tbWjGohNZx80A/mHMNTLpeNs47C9kYOykUrNdFzmnmfa3qkHQXjZRZ5B+3s6elRsVjUZz7zGb3/\n/e9XR0eH0cC4J+TRp+9ZP2S2KpWKbSCLQ09wHgwGLVjOZrN2X+Fw2Li/pM39/RMc+o4qPA9rxhtm\nH9Rxv6wrn4FEBoPBoO677z6jdPksI2vz8PDQWmWyXwjImEfeqKEKBAJNQRAd9TylwWeIyXIdHR1p\na2vLzoFOYe1Go1HbZd6jkjiCyDX3BIiA7uP6PkBEr5TLZa2urlqGjgDP14/5pgKMvZ/jH9Tj+22n\nyJAgb8lkUgsLC8rlcpZBmJmZ0djYmG7cuGFzypygi1tbQdO1koC2r6/PipdB7qEmSTInFCce2iNO\nEmgzAXBbW5vJ59bWlhKJhNVeEGQsLi6qp6dHtVqj6cnh4aEh9KlUSsVi0QK2vr4+q2HgOdF7jIUH\n+ACOWjMy6CqpeSNudJ4PVDxzguYaBHFev3KN+fl5bW1tWZt4wCyYCmQZ2BFeauhpv6nh9evXderU\nKQPLcFxZQx78ktRUM4qOpMaULMOJEyfseaDHUieUTCat7gQ6N5kWNmfGRwH09foau4X+JJOOfPX2\n9iocDmtwcFAvv/yyKpWKNjc3TRdtbm4qk8koFotpdXVVx8fH2tjYMNCXucfx3tvb0/DwsAUBbMpN\nwxnkYn5+XmfPnm2yY9hM1hd/e1q+70IIyOaDXf7nWfkM+pRACdlo9TV9AO0BTfy0trY266wGhSsQ\nCCibzWpkZETRaNQ2V+3t7dX8/LwFf729vXryySebZNsHnwQ5fX19xszxutmPCWPmxyccDltQMDk5\naZSwfD6veDxuWadUKqW9vT2tr6/bBrJkIKvVqtLptGKxmD75yU9qYmLCqO40sNrb29Pb3va2Jp2W\nyWTMHra1tRm18ejoyDrQrqysqK+vz9qj/9AP/ZDa2tqMbVIsFi1gnZyc1K1bt7S3t/eaGvrv9fj3\n1tyka7Xa+p2/1yWl7/w9LOkb7nPLkkYkHd/5m2PlzuuvOaiXwYHc3t62FoSSDOVFYXhqCmiJD0ha\nefmt6Wp+I3DeyZJkqU0WHwvHBzitQRKGS2pG53mf+/XOL07Lww8/rB/90R+19DQdruBh+tavUMcq\nlYrVt1B0WqlUND4+ru3tbSvaKpVKunz5soaGhhQO1/ueb25u6ujoSC+//LKuXLmi3d1dXbhwQYVC\nQQ8++KDOnz9v9w9aBcWmXC5bO+qXX37ZMiTQHlD6tVp9Y8X+/v4mrmehULDajp/6qZ8ylDIUCpnA\neyeYIID0PUVsL7/8siKRiK5evap0Om2tVCORiG2cFg6HtbS0pHQ6bc4Bm53967/+q9LptF5++WUN\nDAwYhQDHg2ANRB6jRF0NTjeZDAwwwYynZnhUyis2b5C8DPt6La/goDp5R6a7u1vvfve7mzYepf7I\nKyKQJbJ45XJZQ0ND1kVub2/P6GrValVra2v2PH19fUqlUlpfX2+iDWAsaEqAoYfr69eVz3QxFjw/\nTjvABYEYso5CbzUKfp8c5mV/f1/hcH0n58XFRctqMabMq59DHzSwrqCo5fN5u2+KqDGy1Kkxr6xp\nxpDXcATI5hB87e7uGpKGnmMtYyTa2tpsjQQCAaMDeGfx6OhI6+vrGhwctPHFYaRDltTIADKOXhb+\nOzr+0+zUpz/9aQNhdnd39Rd/8RfmuBwdHWlubk4jIyOWCZ6dnVUoVO/yxC7dOOxHR0caGRlpahKA\nQ7q1taWuri5lMhm1t9c3loT6wcaT2I3bt29bFpVi6r6+Ph0dHVlwT4YFGX7ssccUDNY3WaSejeCK\n2sa2tnpx9tWrV60LEy3kuU/sIuAZjg66TWoUK7NO0Rm0pYY5cHR0ZBucgt7jTOfzeWWzWR0dHSmd\nTuvg4EADAwNKJBJ2HfwHv/6gX+GEeZoVuoU1yvokEMMWhEIhTU5ONjm6gBseUIS94TMivmsklC6A\nHknmXAaDQW1vbxv1p62tzTI0Kysr6unp0fr6elPNHjKHLva1gT6rzQ+ZDAAZQMzh4WGjIrFBLQ4t\n1Eo2EgYs4ZzJZFK5XM5oTzRiApzN5/Nml8PhsKampkxO0Pue1YBu9b5WtVq14BQwyNdHo3/RvXRG\n8/Qu9CROOAfyy+d8cMjfzC8MGvYIIjiMRqNaWFhQrVazbQg8KEdmnzXDOsemRKNR5XI5q0H2IBf3\nwv3f7XWyZbFYTGtra+rq6tL58+ctoMMu0KVWku2B5Wuy2LPogQceUDQatb1xarWaNjY2NDo6aray\nVCopnU4rn8+bPxmJRLS+vm6bdM7Pz1v9IAyjW7duqVgs6h3veIfZYAKbeDxuzVUA38ly03XSy8q3\nO/6bGwrUarVaIBD4DyNqe6eF/3EwUSaSmiaaBeD56gilL0T0kS8C62srQIRxqnCmubavl/A1Aq33\nTrZAajhjXvmBnHAtEO3Dw0N97Wtf0+nTpzU2NtakWHzXNqlR08P94ORyzr/927/V7du3tb+/r2Qy\nqWg0qlQqpXQ6rcuXL5vQ0TkHxG92dlbLy8tG9aLYiyAM9B4E/8EHHzRBpSvM0NCQdVbxGSqUBp16\nancoEnRbg3pBe0yEGxQ6EGh0jWHDs83NTQ0PD1vqlsXHjshQKjDuh4eHeutb3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Tk7542aMWoAQ8G+MmNfYMCYXqRa+xWMwUNcgRhttnGDxKgPPnHXOeuVgsGuLo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\nBfvNObIn8B+8w729jAo6bP68ueABaMOn9EGDtOr0fMv6BwcHrZgaHoU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RH4V4PeryyBDf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6fXRgcmXBtdA801A1r1K3/XM2rK5XJLloj0cw0w8lq0HuQ56O85HI6Wte3q6sLq6qowdfr6+pDL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2ojyaBeO1XNL+d9IVjBz0+lUpiZmRE7YjabZcgmK1Vut1uG4ObzeeFQWywWxONxcRRMfHRS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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108ead490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[14, 7])\n",
    "plt.subplot(121), plt.imshow(image, cmap='gray'), plt.title('original')\n",
    "plt.subplot(122), plt.imshow(result, cmap='gray'), plt.title('after histogram equalization')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also worth mentioning: to fight very small / large numbers, use `numpy.clip`. Simple and very handy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-15, -10,   0,  10,  15])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.clip([-100, -10, 0, 10, 100], a_min=-15, a_max=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-5 -4 -3 -2 -1  0  1  2  3  4]\n",
      "[0 0 0 0 0 0 1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(-5, 5)\n",
    "print(x)\n",
    "print(x.clip(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Broadcasting, numpy.newaxis\n",
    "\n",
    "[Broadcasting](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) is very useful trick, which simplifies many operations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Weighted covariation matrix\n",
    "numpy has `cov` function, but it doesn't support weights of samples. Let's write our own covariation matrix.\n",
    "\n",
    "Let $X_{ij}$ be the original data. $i$ is indexing samples, $j$ is indexing features.\n",
    "$COV_{jk} = \\sum_i X_{ij} w_i X_{ik}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.random.normal(size=[100, 5])\n",
    "weights = np.random.random(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def covariation(data, weights):\n",
    "    weights = weights / weights.sum()\n",
    "    return data.T.dot(weights[:, np.newaxis] * data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.98246422, -0.09213933,  0.04220243, -0.05215392,  0.07939284],\n",
       "       [-0.09213933,  1.22118146, -0.10138206,  0.23070906,  0.14642467],\n",
       "       [ 0.04220243, -0.10138206,  0.84470403, -0.00338101, -0.1249949 ],\n",
       "       [-0.05215392,  0.23070906, -0.00338101,  1.10425395, -0.11689576],\n",
       "       [ 0.07939284,  0.14642467, -0.1249949 , -0.11689576,  1.37570908]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cov(data.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  9.80919618e-01,  -9.15155980e-02,   3.81764158e-02,\n",
       "         -5.88816111e-02,   8.46305808e-02],\n",
       "       [ -9.15155980e-02,   1.20898034e+00,  -1.00238681e-01,\n",
       "          2.28662578e-01,   1.44743583e-01],\n",
       "       [  3.81764158e-02,  -1.00238681e-01,   8.37825668e-01,\n",
       "         -1.91880751e-04,  -1.26370312e-01],\n",
       "       [ -5.88816111e-02,   2.28662578e-01,  -1.91880751e-04,\n",
       "          1.09955816e+00,  -1.21007564e-01],\n",
       "       [  8.46305808e-02,   1.44743583e-01,  -1.26370312e-01,\n",
       "         -1.21007564e-01,   1.36634581e+00]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covariation(data, np.ones(len(data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.08429417, -0.11032936,  0.09253106, -0.006654  ,  0.14981251],\n",
       "       [-0.11032936,  1.04538348, -0.02432767,  0.22118083, -0.06601326],\n",
       "       [ 0.09253106, -0.02432767,  0.95356108, -0.05339915, -0.08393104],\n",
       "       [-0.006654  ,  0.22118083, -0.05339915,  1.09609127, -0.17836458],\n",
       "       [ 0.14981251, -0.06601326, -0.08393104, -0.17836458,  1.22027334]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covariation(data, weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "alternative way to do this is via `numpy.einsum`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.08429417, -0.11032936,  0.09253106, -0.006654  ,  0.14981251],\n",
       "       [-0.11032936,  1.04538348, -0.02432767,  0.22118083, -0.06601326],\n",
       "       [ 0.09253106, -0.02432767,  0.95356108, -0.05339915, -0.08393104],\n",
       "       [-0.006654  ,  0.22118083, -0.05339915,  1.09609127, -0.17836458],\n",
       "       [ 0.14981251, -0.06601326, -0.08393104, -0.17836458,  1.22027334]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.einsum('ij, ik, i -> jk', data, data, weights / weights.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pearson correlation\n",
    "\n",
    "One more example of broadcasting: computation of Pearson coefficient.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.08411948,  0.04632621, -0.0500719 ,  0.0682904 ],\n",
       "       [-0.08411948,  1.        , -0.09982027,  0.19867356,  0.1129694 ],\n",
       "       [ 0.04632621, -0.09982027,  1.        , -0.00350074, -0.11595159],\n",
       "       [-0.0500719 ,  0.19867356, -0.00350074,  1.        , -0.09484206],\n",
       "       [ 0.0682904 ,  0.1129694 , -0.11595159, -0.09484206,  1.        ]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(data.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.08411948,  0.04632621, -0.0500719 ,  0.0682904 ],\n",
       "       [-0.08411948,  1.        , -0.09982027,  0.19867356,  0.1129694 ],\n",
       "       [ 0.04632621, -0.09982027,  1.        , -0.00350074, -0.11595159],\n",
       "       [-0.0500719 ,  0.19867356, -0.00350074,  1.        , -0.09484206],\n",
       "       [ 0.0682904 ,  0.1129694 , -0.11595159, -0.09484206,  1.        ]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def my_corrcoef(data, weights):\n",
    "    data = data - np.average(data, axis=0, weights=weights)\n",
    "    shifted_cov = covariation(data, weights)\n",
    "    cov_diag = np.diag(shifted_cov)\n",
    "    return shifted_cov / np.sqrt(cov_diag[:, np.newaxis] * cov_diag[np.newaxis, :])\n",
    "\n",
    "my_corrcoef(data, np.ones(len(data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pairwise distances between vectors\n",
    "\n",
    "we're given a set of vectors (say, 1000 vectors). The problem is to compute their pairwise distances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = np.random.normal(size=[1000, 100])\n",
    "distances = ((X[:, np.newaxis, :] - X[np.newaxis, :, :]) ** 2).sum(axis=2) ** 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "also, since algebra is you friend, you can do it times faster: \n",
    "$$||x_i - x_j||^2 = ||x_i||^2 + ||x_j||^2 - 2 (x_i, x_j) $$\n",
    "so dot products is the only thing you need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "products = X.dot(X.T)\n",
    "distances2 = products.diagonal()[:, np.newaxis] + products.diagonal()[np.newaxis, :]  - 2 * products\n",
    "distances2 **= 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... but keep in mind there is __sklearn.metrics.pairwise__ which does it for you and has different options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import pairwise_distances\n",
    "distances_sklearn = pairwise_distances(X)\n",
    "\n",
    "np.allclose(distances, distances_sklearn), np.allclose(distances2, distances_sklearn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. numpy.unique, numpy.searchsorted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting categories to small integers\n",
    "\n",
    "Despite categories are easier to read, operating with them is much faster after you represent them as small numbers <br /> (for instance, in range [0, n_categories - 1])\n",
    "\n",
    "First generate some categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cd' 'bi' 'ag' ..., 'gn' 'ik' 'kl']\n"
     ]
    }
   ],
   "source": [
    "from itertools import combinations\n",
    "possible_categories = map(lambda x: x[0] + x[1], list(combinations('abcdefghijklmn', 2)))\n",
    "categories = np.random.choice(possible_categories, size=10000)\n",
    "print(categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we use __numpy.unique__ to convert them to numbers (pay attention to `return_inverse=True`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unique_categories, new_categories = np.unique(categories, return_inverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ab' 'ac' 'ad' 'ae' 'af' 'ag' 'ah' 'ai' 'aj' 'ak' 'al' 'am' 'an' 'bc' 'bd'\n",
      " 'be' 'bf' 'bg' 'bh' 'bi' 'bj' 'bk' 'bl' 'bm' 'bn' 'cd' 'ce' 'cf' 'cg' 'ch'\n",
      " 'ci' 'cj' 'ck' 'cl' 'cm' 'cn' 'de' 'df' 'dg' 'dh' 'di' 'dj' 'dk' 'dl' 'dm'\n",
      " 'dn' 'ef' 'eg' 'eh' 'ei' 'ej' 'ek' 'el' 'em' 'en' 'fg' 'fh' 'fi' 'fj' 'fk'\n",
      " 'fl' 'fm' 'fn' 'gh' 'gi' 'gj' 'gk' 'gl' 'gm' 'gn' 'hi' 'hj' 'hk' 'hl' 'hm'\n",
      " 'hn' 'ij' 'ik' 'il' 'im' 'in' 'jk' 'jl' 'jm' 'jn' 'kl' 'km' 'kn' 'lm' 'ln'\n",
      " 'mn']\n",
      "[25 19  5 ..., 69 77 85]\n"
     ]
    }
   ],
   "source": [
    "print(unique_categories)\n",
    "print(new_categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also we can reconstruct old categories if needed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cd' 'bi' 'ag' ..., 'gn' 'ik' 'kl']\n",
      "['cd' 'bi' 'ag' ..., 'gn' 'ik' 'kl']\n"
     ]
    }
   ],
   "source": [
    "print(categories)\n",
    "print(unique_categories[new_categories])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mapping new categories\n",
    "\n",
    "When we need to map new categories using the same lookup table, we can use `numpy.searchsorted`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['gn' 'hn' 'be' ..., 'il' 'bk' 'bl']\n",
      "['gn' 'hn' 'be' ..., 'il' 'bk' 'bl']\n"
     ]
    }
   ],
   "source": [
    "categories2 = np.random.choice(possible_categories, size=10000)\n",
    "new_categories2 = np.searchsorted(unique_categories, categories2)\n",
    "print(categories2)\n",
    "print(unique_categories[new_categories2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking values present in the lookup\n",
    "\n",
    "Usually, we use `set` to have many checks that each element exists in array. But __numpy.unique__ is better option - it works dozens times faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False], dtype=bool)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.in1d(['ab', 'ac', 'something new'], unique_categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling missing categories\n",
    "\n",
    "When some new category appears in test data, this is always a trouble. Possible solutions:\n",
    "\n",
    "* map randomly to one of present categories (this, i.e. is done by hashing trick)\n",
    "* make a special category 'unknown' where all new values will be mapped. Then special value may be chosen for it.\n",
    "  This approach is more general, but requires more efforts.\n",
    "\n",
    "Below you can find the transformer for second approach:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class CategoryMapper:\n",
    "    def fit(self, categories):\n",
    "        self.lookup = np.unique(categories)\n",
    "        return self\n",
    "        \n",
    "    def transform(self, categories):\n",
    "        \"\"\"Converts categories to numbers, 0 is reserved for new values (not present in fitted data)\"\"\"\n",
    "        return (np.searchsorted(self.lookup, categories) + 1) * np.in1d(categories, self.lookup)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CategoryMapper().fit(categories).transform([unique_categories[0], 'abc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. numpy.percentile, numpy.bincount\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting data into bins\n",
    "\n",
    "Splitting data into bins is frequently necessary. Simplest way done e.g. by `numpy.histogram` is splitting region with even bins of same size.\n",
    "\n",
    "This usually works poorly with distributions with long tails (or strange shape) and requires additional transformation to avoid empty or too large bins.\n",
    "\n",
    "However, for most cases creating the bins with equal number of events gives more convenient and stable results.\n",
    "Selecting the edges of bins can be done with `numpy.percentile`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VvavftiTbAarqoWbb08CDwP8Anquqdzb1HwM+UFX/fJG/V4tfWXMUuInBV91c2O1ezilp\n0iShqoa+7HBZQz3NmP0ZHwXOXPFzALgnyZVJNgJTwExVnQReTbK5Odl7L/Dkcl5bkrQyA4d6kuwD\nPgC8LcnLdHvw00lupts9/iHwKYCqmk2yH5gFTgPb6mwXeRvwBLAGOFhVT6/ye5EkDWGooZ5RcqhH\nkpZmJEM9kqSLl8EvSS1j8EtSyxj8ktQyBr8ktYyPZV6ifs/r94ofSRcDg3/JzhfuPqtf0sXBoR5J\nahmDX5JaxuCXpJYx+CWpZQx+SWoZg1+SWsbgl6SWMfglqWUMfklqmYHBn+SLSRaSHOmpuybJoSQv\nJnkmydU923YkOZ7kWJLbeupvTXKk2fbI6r8VSdIwhunxfwnYck7dduBQVd0IPNusk2QTcDewqTnm\n0Zx9uM1uYGtVTQFTSc79m5KkERgY/FX1beCn51TfDuxpynuAO5vyHcC+qjpVVSeAl4DNzeTsV1XV\nTLPf3p5jLhlJ+i6SNAmWO8a/tqoWmvICsLYpXw/M9ew3B6xbpH6+qb/EVJ9FkibDip/OWVXVnSB9\nNe3sKU83iyQJoNPp0Ol0ln38coN/Icm1VXWyGcZ5pamfBzb07Leebk9/vin31s+f/8/vXGazJOnS\nNz09zfT09C/Wd+3ataTjlzvUcwC4rynfBzzZU39PkiuTbASmgJmqOgm8mmRzc7L33p5jJEkjNLDH\nn2Qf8AHgbUleBn4XeAjYn2QrcAK4C6CqZpPsB2aB08C2Ojst1TbgCWANcLCqnl7dtyJJGkYmbbrA\n7vmCxdp0FLiJ/idKM8btg4+dtM9a0qUhCVU19KWD3rkrSS1j8EtSyxj8ktQyBr8ktYzBL0ktY/BL\nUsus+JENGt6gB7V5uaekUTD4R2rQPQKSdOE51CNJLWPwS1LLGPyS1DIGvyS1jCd3J4hX/UgaBYN/\nonjVj6QLz6EeSWoZg1+SWmZFwZ/kRJLvJTmcZKapuybJoSQvJnkmydU9++9IcjzJsSS3rbTxkqSl\nW2mPv4Dpqrqlqt7T1G0HDlXVjcCzzTpJNgF3A5uALcCjSfzFIUkjthrBe+5Zx9uBPU15D3BnU74D\n2FdVp6rqBPAS8B4kSSO1Gj3+byb5TpJPNnVrq2qhKS8Aa5vy9cBcz7FzwLoVvn6rJOm7SNIwVno5\n5/ur6sdJ/g5wKMmx3o1VVd3J08/rPNt29pSnm0Ve7ikJoNPp0Ol0ln18VuumoCQPAq8Bn6Q77n8y\nyXXAc1X1jiTbAarqoWb/p4EHq+r5c/5OLR5wR4GbGBx+49o+ztfubvcGL6mdklBVQ/f+lj3Uk+RN\nSa5qyn8buA04AhwA7mt2uw94sikfAO5JcmWSjcAUMLPc15ckLc9KhnrWAv+hGVu+AvizqnomyXeA\n/Um2AieAuwCqajbJfmAWOA1sK7uokjRyqzbUs1oc6lnJ9vObtP+fJa2epQ71+KyeS0q/LyVJ6vIG\nKklqGYNfklrGoZ6W8Fn/ks4w+FvDm78kdTnUI0ktY/BLUssY/JLUMo7xC/Dkr9QmBr8anvyV2sLg\n11D8RSBdOgx+DclfBNKlwuDXqvAXgXTxMPi1Svr/IvCLQZocBr9GxKEiaVKM/Dr+JFuSHEtyPMm/\nGvXrazI5ibw0OiMN/iSXA/8G2AJsAj6W5J2jbMPFozPuBoxYnWfp/6XQti+GlUywfanxs1i+Uff4\n3wO8VFUnquoU8OfAHSNuw0WiM+4GTJAH6ffF0M+gL42L7YvDsDvLz2L5Rj3Gvw54uWd9Dtg84jbo\nEjM4vFd24nklPGmtSTTq4B/qX8Fb3vKPf6Xu9df/mtdeW/X26JKw0hPHF26e5QvxpbJr166h9uv3\npbMa7fJL7eI10snWk7wX2FlVW5r1HcDrVfVHPfv4X5MkLdFSJlsfdfBfAfw34B8BPwJmgI9V1Q9G\n1ghJarmRDvVU1ekk/wL4BnA58LihL0mjNdIevyRp/CZmIhZv7OpKsiHJc0mOJvl+ks+Mu03jluTy\nJIeTPDXutoxTkquTfDXJD5LMNufMWinJjubfyJEkX0nyt8bdplFJ8sUkC0mO9NRdk+RQkheTPJPk\n6n5/YyKC3xu7fskp4IGq+jXgvcDvtPizOON+YJYhrwq7hD0CHKyqdwK/DrRymDTJDcAngXdX1bvo\nDhvfM842jdiX6GZlr+3Aoaq6EXi2WT+viQh+vLHrF6rqZFW90JRfo/uP+/rxtmp8kqwHPgw8Rosf\n6pPkrcA/qKovQvd8WVX9bMzNGpdX6XaQ3tRcMPImYH68TRqdqvo28NNzqm8H9jTlPcCd/f7GpAT/\nYjd2rRtTWyZG07O5BXh+vC0Zqy8AnwVeH3dDxmwj8L+SfCnJXyX5d0neNO5GjUNV/W/g88D/pHt1\n4P+pqm+Ot1Vjt7aqFpryArC2386TEvxt/wn/K5K8GfgqcH/T82+dJB8BXqmqw7S4t9+4Ang38GhV\nvRv4vwz4OX+pSvJ3gX8J3ED31/Cbk/zTsTZqglT3ip2+mTopwT8PbOhZ30C3199KSd4AfA3406p6\nctztGaP3Abcn+SGwD/iHSfaOuU3jMgfMVdV/bda/SveLoI1+A/jPVfWTqjoN/Hu6/6202UKSawGS\nXAe80m/nSQn+7wBTSW5IciVwN3BgzG0ai3TvpX8cmK2qfz3u9oxTVX2uqjZU1Ua6J+++VVWfGHe7\nxqGqTgIvJ7mxqfot4OgYmzROx4D3JlnT/Hv5Lbon/9vsAHBfU74P6NthnIiJWLyx65e8H/g48L0k\nh5u6HVX19BjbNCnaPiT4aeDPms7Rfwf+2ZjbMxZV9d3ml9936J77+Svg3463VaOTZB/wAeBtSV4G\nfhd4CNifZCtwArir79/wBi5JapdJGeqRJI2IwS9JLWPwS1LLGPyS1DIGvyS1jMEvSS1j8EtSyxj8\nktQy/x8I5MIs2Z54zAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108d71890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.exponential(size=20000)\n",
    "_ = plt.hist(x, bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nb7aZrFvvkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c06150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "edges = np.percentile(x, np.linspace(0, 100, 20))\n",
    "_ = plt.hist(x, bins=edges, normed=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the number of events within each bin is equal now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the index of bin\n",
    "\n",
    "Use `numpy.searchsorted` to compute the index of bin for each value in `x`. (`numpy.digitize` is the other option, it does the same).\n",
    "\n",
    "`numpy.searchsorted` assumes that first array is sorted and uses binary search, so it is effective even for large amount of bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  2, 16, ...,  7,  9,  3])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.searchsorted(edges, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Counter\n",
    "\n",
    "Need to know number of occurences for each category? Use `numpy.bincount` to compute the statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25, 19,  5, ..., 69, 77, 85])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will compute how many times each category present in the data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([112, 105, 127, 112, 118, 107, 129,  94, 118, 105, 109, 103,  97,\n",
       "       108, 110,  97, 103, 111, 103, 102, 108, 115, 121,  98, 121, 115,\n",
       "       105, 113, 109,  97,  91, 109, 109, 107, 114, 123,  99,  98,  92,\n",
       "       117, 103,  94, 111, 113, 125,  98, 110, 122, 108, 117, 106, 100,\n",
       "       111, 108, 106, 130, 116,  97, 112, 106, 103, 103, 109, 107, 106,\n",
       "       121, 107, 117, 115, 113, 109, 107, 122, 123, 117, 104,  99, 125,\n",
       "       110, 122,  93, 105, 112, 118, 116, 105, 110, 112, 123, 116, 127])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(new_categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the same done with `collections.Counter` (which is much slower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([112, 105, 127, 112, 118, 107, 129,  94, 118, 105, 109, 103,  97,\n",
       "       108, 110,  97, 103, 111, 103, 102, 108, 115, 121,  98, 121, 115,\n",
       "       105, 113, 109,  97,  91, 109, 109, 107, 114, 123,  99,  98,  92,\n",
       "       117, 103,  94, 111, 113, 125,  98, 110, 122, 108, 117, 106, 100,\n",
       "       111, 108, 106, 130, 116,  97, 112, 106, 103, 103, 109, 107, 106,\n",
       "       121, 107, 117, 115, 113, 109, 107, 122, 123, 117, 104,  99, 125,\n",
       "       110, 122,  93, 105, 112, 118, 116, 105, 110, 112, 123, 116, 127])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "np.array(sorted(Counter(new_categories).items()))[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replace category with number of occurences\n",
    "\n",
    "There are several tricks important to process the categorical features. \n",
    "Most known one is one-hot encoding, but let's speak about others.\n",
    "\n",
    "One of tricks is to replace in each event category (which cannot be processed by most algorithms) with a number of times it presents in data. \n",
    "\n",
    "This can be written in __numpy__ one-liner:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([115, 102, 107, ..., 113, 125, 105])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(new_categories)[new_categories]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Averaging predictions over category\n",
    "\n",
    "Another important trick: replace category with average prediction for events from this category.\n",
    "\n",
    "This is more tricky, since we need to add weights to `numpy.bincount`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predictions = np.random.normal(size=len(new_categories))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.03873642, -0.00404972,  0.01078471, ..., -0.13723158,\n",
       "       -0.13214773,  0.12129524])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means_over_category = np.bincount(new_categories, weights=predictions) / np.bincount(new_categories)\n",
    "means_over_category[new_categories]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variation of predictions over category\n",
    "\n",
    "The same trick can be applied to measure deviation after invloving some math:\n",
    "$$ \\mathbb{V}x = \\mathbb{E}x^2 - (\\mathbb{E}x)^2 $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.85843132,  0.94388082,  0.96350536, ...,  0.8831292 ,\n",
       "        1.01031933,  0.67702705])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means_of_squares_over_category = np.bincount(new_categories, weights=predictions**2) / np.bincount(new_categories)\n",
    "var_over_category = means_of_squares_over_category - means_over_category ** 2\n",
    "var_over_category[new_categories]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also assign weights to events and compute weighted mean / weighted variation using the same functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Numpy.ufunc.at\n",
    "\n",
    "To proceed futher, we need to know that there is class of binary (and unary) functions: `numpy.ufuncs`\n",
    "\n",
    "Examples: `numpy.add`, `numpy.subtract`, `numpy.multiply`, `numpy.maximum`, `numpy.minimum`\n",
    "\n",
    "In the following example we cook a new column with maximal predictions over category using `ufunc.at`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.87279069  2.43059696  2.04921886 ...,  2.62513163  2.81520326\n",
      "  1.72020708]\n"
     ]
    }
   ],
   "source": [
    "max_over_category = np.zeros(new_categories.max() + 1) - np.infty\n",
    "np.maximum.at(max_over_category, new_categories, predictions)\n",
    "\n",
    "print(max_over_category[new_categories])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Important note: `numpy.add.at` and `numpy.bincount` are very similar, but latter is dozens of times faster. Always prefer it to `numpy.add.at`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 1.63 ms per loop\n",
      "10000 loops, best of 3: 33 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit np.add.at(max_over_category, new_categories, predictions)\n",
    "%timeit np.bincount(new_categories, weights=predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multidimensional statistics: numpy.add.at and smart indexing\n",
    "\n",
    "When need to compute number of times each combination met, there are two ways:\n",
    "1. convert couple of variables to new variable, e.g. by feature1 * (numpy.max(feature2) + 1) + feature2\n",
    "2. create multidimensional table\n",
    "\n",
    "I prefer first, code below is demonstration of second approach and it's benefits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "first_category = np.random.randint(0, 100, len(new_categories))\n",
    "second_category = np.random.randint(0, 100, len(new_categories))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "counters = np.zeros([first_category.max() + 1, second_category.max() + 1])\n",
    "np.add.at(counters, [first_category, second_category], 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now some useful things we can do with colected statistic. First thing is create feature, for which we use fancy indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  3., ...,  4.,  3.,  1.])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counters[first_category, second_category]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also infer different statistics like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 122.,  102.,   82., ...,  112.,  118.,  100.])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# occurences of second category\n",
    "counters.sum(axis=0)[second_category]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  4.,  3., ...,  4.,  4.,  7.])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# maximal occurences of second category with same value of first category\n",
    "counters.max(axis=0)[second_category]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0., ...,  4.,  4.,  1.])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of occurences of second category with fixed first category:\n",
    "counters[42, second_category]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* you are not limited to `numpy.add`, remember there are other ufuncs.\n",
    "* Few space to keep whole matrix? Use `scipy.sparse` if there are too many zero elements. \n",
    "  Alternatively, you can use matrix decompositions to keep approximation of matrix and compute result on-the-fly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing ROC curve\n",
    "\n",
    "As an important exercise, let's implement the computation of ROC curve. (interactive demo of ROC curve may be found [here](https://arogozhnikov.github.io/2015/10/05/roc-curve.html) )\n",
    "\n",
    "We will write the same as `sklearn.metrics.roc_curve` (actually, simplified version of it).\n",
    "\n",
    "ROC curve takes three arrays: labels (binary, 0 or 1), predictions (real-valued) and weights.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def roc_curve(labels, predictions, sample_weight):\n",
    "    sig_weights = sample_weight * (labels == 1)\n",
    "    bck_weights = sample_weight * (labels == 0)\n",
    "    thresholds, predictions = np.unique(predictions, return_inverse=True)\n",
    "    tpr = np.bincount(predictions, weights=sig_weights)[::-1].cumsum()\n",
    "    fpr = np.bincount(predictions, weights=bck_weights)[::-1].cumsum()\n",
    "    tpr /= tpr[-1]\n",
    "    fpr /= fpr[-1]\n",
    "    return fpr, tpr, thresholds[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(1)\n",
    "size = 1000\n",
    "labels = (np.random.random(size) > 0.5) * 1\n",
    "predictions = np.random.normal(size=size) + labels\n",
    "weights = np.random.exponential(size=size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fpr1, tpr1, thr1 = roc_curve(labels, predictions, weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check how result looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.text.Text at 0x109956e90>, <matplotlib.text.Text at 0x10995c190>)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D79y5uavnO3hwXdfPd1xXzygtX3svMSzeT2wvsVrt21d0AkkaHFsjJEmSVErO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\nshCWJElSKVkIS5IkqZQshCVJklRK/x91gL1gGwmS4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118893ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[12, 5])\n",
    "plt.subplot(121)\n",
    "plt.hist(predictions[labels == 0], bins=20, alpha=0.5)\n",
    "plt.hist(predictions[labels == 1], bins=20, alpha=0.5)\n",
    "plt.subplot(122)\n",
    "plt.plot(fpr1, tpr1)\n",
    "plt.xlabel('FPR'), plt.ylabel('TPR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True, True)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sometimes sklearn adds one more element to all arrays.\n",
    "# I have no idea why this is needed and when, but in this case all answers turn to False\n",
    "from sklearn.metrics import roc_curve as sklearn_roc_curve\n",
    "fpr2, tpr2, thr2 = sklearn_roc_curve(labels, predictions, sample_weight=weights)\n",
    "np.allclose(fpr1, fpr2), np.allclose(tpr1, tpr2), np.allclose(thr1, thr2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weighted Kolmogorov-Smirnov distance\n",
    "\n",
    "ROC curve is an important object by itself and freqently used in ML to compare distribution, \n",
    "but the magic is that it contains all order-based information about original distributions.\n",
    "\n",
    "Next we compute KS distance based on ROC curve:\n",
    "\n",
    "$$ KS = \\max_x \\left| F_1(x) - F_2(x) \\right| $$\n",
    "\n",
    "(Order-based information == information, which is preserved under monotonic transformations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ks_2samp(distribution1, distribution2, weights1, weights2):\n",
    "    labels = np.array([0] * len(distribution1) + [1] * len(distribution2))\n",
    "    predictions = np.concatenate([distribution1, distribution2])\n",
    "    weights = np.concatenate([weights1, weights2])\n",
    "\n",
    "    fpr, tpr, _ = roc_curve(labels, predictions, sample_weight=weights)\n",
    "    return np.max(np.abs(fpr - tpr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data1 = np.random.normal(loc=0, size=size)\n",
    "data2 = np.random.normal(loc=1, size=size)\n",
    "weights1 = np.random.exponential(size=size) \n",
    "weights2 = np.random.exponential(size=size) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Compare results__ with standard function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.416\n",
      "0.416\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ks_2samp as ks_2samp_scipy\n",
    "print( ks_2samp(data1, data2, weights1=np.ones(size), weights2=np.ones(size)) )\n",
    "print( ks_2samp_scipy(data1, data2)[0] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Important moment that written function supports weights, while scipy version doesn't."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__TODO:__ optimal F1, optimal accuracy based on ROC curve."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Many things can be written in numpy in efficient way, moreover resulting code will be short and (surpise!) more readable. \n",
    "\n",
    "But this skill requires much practice.\n",
    "\n",
    "## See also\n",
    "\n",
    "* Second part of post: [numpy tips and tricks 2](/2015/09/30/NumpyTipsAndTricks2.html)\n",
    "* I used example from [histogram equalization with numpy](http://www.janeriksolem.net/2009/06/histogram-equalization-with-python-and.html)\n",
    "* [Numpy log-likelihood benchmark](/2015/09/08/SpeedBenchmarks.html)\n"
   ]
  }
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